{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "OZEjehlD9jb3"
      },
      "source": [
        "# Importing packages\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAmyY8EpN4Ix",
        "outputId": "e8cb6ce6-0010-483e-eed5-f03ea0317762"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.2.0)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2022.10.31)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.65.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (4.3.1)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.10.1)\n",
            "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim) (6.3.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.10.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (3.1.0)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ],
      "source": [
        "! pip install nltk\n",
        "! pip install gensim\n",
        "! pip install imbalanced-learn\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import gensim\n",
        "from gensim.models import Word2Vec\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "import warnings\n",
        "from collections import defaultdict  # For word frequency\n",
        "import spacy  # For preprocessing\n",
        "warnings.filterwarnings(action = 'ignore')\n",
        "\n",
        "\n",
        "import random\n",
        "from sklearn.metrics import matthews_corrcoef, cohen_kappa_score, brier_score_loss, roc_auc_score, roc_curve, precision_score, auc, precision_recall_curve, classification_report, average_precision_score, matthews_corrcoef\n",
        "from sklearn import svm\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import StratifiedKFold, LeaveOneOut\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import KFold\n",
        "import copy\n",
        "\n",
        "from imblearn.over_sampling import SMOTE, RandomOverSampler\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "# import urllib.request\n",
        "# from urllib.error import HTTPError\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "gfVv-5RTUQZt"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "\n",
        "from collections import defaultdict  # For word frequency\n",
        "\n",
        "import spacy  # For preprocessing\n",
        "\n",
        "warnings.filterwarnings(action = 'ignore')\n",
        "\n",
        "import random\n",
        "from sklearn import svm\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import keras\n",
        "from keras.layers import Conv1D, MaxPool1D, Flatten\n",
        "from keras.models import Sequential, Model\n",
        "from sklearn.model_selection import train_test_split\n",
        "import random\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from tqdm import tqdm\n",
        "import cv2\n",
        "from numpy import random as rng\n",
        "from sklearn.utils import shuffle\n",
        "import pickle\n",
        "import time\n",
        "\n",
        "from tensorflow.keras.layers import Input, Lambda, Conv2D, MaxPooling2D, BatchNormalization, Dense, Flatten, Activation, Dropout\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras import backend as K\n",
        "\n",
        "import re\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras import backend as K\n",
        "from keras.layers import Activation\n",
        "from keras.layers import Input, Lambda, Dense, Dropout, Convolution2D, MaxPooling2D, Flatten\n",
        "from keras.models import Sequential, Model\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from keras import optimizers\n",
        "\n",
        "import matplotlib.image as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from keras import callbacks\n",
        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, ReduceLROnPlateau, TensorBoard\n",
        "import os\n",
        "from keras.models import Model,load_model\n",
        "import json\n",
        "from keras.models import model_from_json, load_model\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import tensorflow as tf\n",
        "# import keras\n",
        "from keras.layers import Input, Conv1D, Lambda, Dense, Flatten,MaxPooling1D, Concatenate\n",
        "from keras.models import Model, Sequential\n",
        "from keras.regularizers import l2\n",
        "from keras import backend as K\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.optimizers import Adam, SGD\n",
        "from keras.losses import binary_crossentropy\n",
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Dense, Dropout, Subtract, Lambda, concatenate\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "E4DPQo6ciHJF"
      },
      "source": [
        "# Data loading"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "E1K3aImv-zpt"
      },
      "source": [
        "The associations are based RepoDB database and collected from golden standard database of our prior study, DRP-VEM."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CHIpDlp6WXO5"
      },
      "outputs": [],
      "source": [
        "ass = pd.read_csv(\"my_repodb.txt\")\n",
        "ass.set_index(\"Unnamed: 0\", inplace=True)\n",
        "disgenet = pd.read_csv(\"Gen-Dis-association-Disgenet-2022 - curated_gene_disease_associations.csv\")\n",
        "\n",
        "# making the drug and disease lists\n",
        "my_disease_list = list(set(ass.ind_id))\n",
        "my_drug_list = list(set(ass.drug_id))\n",
        "drugs=list(set(ass.drug_id))\n",
        "disgenet = disgenet.loc[disgenet['diseaseId'].isin(my_disease_list)]\n",
        "my_disease_list = list(set(disgenet.diseaseId))\n",
        "ass = ass.loc[ass['ind_id'].isin(my_disease_list)]\n",
        "my_drug_list = list(set(ass.drug_id))\n",
        "\n",
        "# making the list of known drug-disease associations\n",
        "\n",
        "A=[]\n",
        "for ix, row in ass.iterrows():\n",
        "  text = str(row.drug_id)+\" has_indicated \"+str(row.ind_id)+\". \"\n",
        "  A.append(text)\n",
        "\n",
        "associations = []\n",
        "for a in A:\n",
        "  associations.append(a)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hbn2fUcAJyim"
      },
      "outputs": [],
      "source": [
        "freq = ass.groupby(['drug_id'])['ind_id'].agg('count').reset_index()\n",
        "freq = freq.sort_values(by='ind_id', ascending=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iYpuJn_1FlvX"
      },
      "outputs": [],
      "source": [
        "genid={}\n",
        "for idx, row in disgenet.iterrows():\n",
        "  # print(row)\n",
        "  if row.geneId in genid.keys():\n",
        "    continue\n",
        "  else:\n",
        "    genid[row.geneId]=row.geneSymbol"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lveVLqkSg1N3"
      },
      "outputs": [],
      "source": [
        "has = pd.read_csv(\"my_repodb.txt\") # Positive data: Known Drug-Disease associations\n",
        "has.set_index(\"Unnamed: 0\", inplace=True)\n",
        "has = has.drop(['drug_name', 'ind_name','NCT','status', 'phase',\n",
        "       'DetailedStatus'], axis=1)\n",
        "has = has.loc[has['ind_id'].isin(my_disease_list)]\n",
        "\n",
        "\n",
        "hasnt = pd.read_csv(\"my_only_negatives.txt\") # Negative data: Unknown Drug-Disease associations when disease is adverse drug reaction\n",
        "hasnt.set_index(\"Unnamed: 0\", inplace=True)\n",
        "hasnt = hasnt.rename(columns={\"Not_indicated_for\":\"ind_id\", \"Drug_id\": \"drug_id\"})\n",
        "hasnt = hasnt.loc[hasnt['ind_id'].isin(my_disease_list)]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49Ts6SkknKOi",
        "outputId": "b07ee8ff-7308-480f-e029-2f95d62ab51e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The number of all known drug disease associations in our dataset:  (748, 2)\n",
            "Number of unique disease in Pos:  141\n",
            "Number of uniques drugs in Pos:  410\n"
          ]
        }
      ],
      "source": [
        "# Positive : Known drug-disease associations\n",
        "print(\"The number of all known drug disease associations in our dataset: \",(has.shape))\n",
        "print(\"Number of unique disease in Pos: \",len(list(set(has.ind_id))) )\n",
        "print(\"Number of uniques drugs in Pos: \", len(list(set(has.drug_id))) )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZXvtRqpmzZA",
        "outputId": "35e7330a-5770-4372-fc5e-9908afe62800"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The number of unknown drug disease associations in our dataset:  (1966, 2)\n",
            "Number of unique disease in Neg:  88\n",
            "Number of uniques drugs in Neg:  361\n"
          ]
        }
      ],
      "source": [
        "# Negatives : Unknown drug-disease associations provided the disease is adverse drug reactoins\n",
        "\n",
        "print(\"The number of unknown drug disease associations in our dataset: \",(hasnt.shape))\n",
        "print(\"Number of unique disease in Neg: \",len(list(set(hasnt.ind_id))) )\n",
        "print(\"Number of uniques drugs in Neg: \", len(list(set(hasnt.drug_id))) )"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "u0-W14GnITTO"
      },
      "source": [
        "The information of diseases are collected using [DisGeNet](https://www.disgenet.org)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CBpV3s6Ep9sX"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "# making the relation of diseases: has_geneid, has_class, has_semantic, has_type\n",
        "\n",
        "C=[]\n",
        "num1=0\n",
        "num2=0\n",
        "num3=0\n",
        "num4=0\n",
        "for idx, row in disgenet.iterrows():\n",
        "  # if row.diseaseId in my_disease_list:\n",
        "      semtype = \"_\".join(row.diseaseSemanticType.split())\n",
        "      clas = row.diseaseClass.split(\";\")\n",
        "      text1 =str(row.diseaseId)+\" has_geneid \"+str(row.geneSymbol)+\". \"\n",
        "      num1+=1\n",
        "      text2 =str(row.diseaseId)+\" has_type \"+str(row.diseaseType)+\". \"\n",
        "      num2+=1\n",
        "      text3 =str(row.diseaseId)+\" has_semantic \"+str(semtype)+\". \"\n",
        "      num3+=1\n",
        "      C.append(text1)\n",
        "      C.append(text2)\n",
        "      C.append(text3)\n",
        "      num4+=len(clas)\n",
        "      for cl in clas:\n",
        "        text =str(row.diseaseId)+\" has_class \"+str(cl)+\". \"\n",
        "        C.append(text)\n",
        "\n",
        "Disease=\"\"\n",
        "for c in C:\n",
        "  Disease= Disease+c\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lJMvNAjbIk8g"
      },
      "outputs": [],
      "source": [
        "gensymbol = set(list(disgenet.geneSymbol))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "p7mdjCe2RWKp"
      },
      "source": [
        "making the drug relations: has_sideeffect, has_target, has_domain, has_atccode, has_chemicalstructure"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t7dgE79vZq2Q"
      },
      "outputs": [],
      "source": [
        "# has_target relation. data is collected using DrugBank\n",
        "T=[]\n",
        "target= pd.read_csv(\"my_target.txt\")\n",
        "target.set_index(\"Unnamed: 0\", inplace=True)\n",
        "# target_name = target.columns\n",
        "for idx, row in target.iterrows():\n",
        "  for c in target.columns:\n",
        "    if row[c] ==1 :\n",
        "      text =str(idx)+\" has_target \"+str(c)+\". \"\n",
        "      T.append(text)\n",
        "target1=\"\"\n",
        "for c in T:\n",
        "  target1= target1+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0HmWoqUJeEbs"
      },
      "outputs": [],
      "source": [
        "g = pd.DataFrame(target.columns, columns=[\"target name\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dbMxbLsMHo45"
      },
      "outputs": [],
      "source": [
        "PPI = pd.read_csv(\"PPI-extracted scores more than 80-cleaned.txt\")\n",
        "p=[]\n",
        "for idx, row in PPI.iterrows():\n",
        "  if row.P1 in target.columns and row.P2 in target.columns:\n",
        "    text = str(row.P1)+' has_ppi_interaction '+ str(row.P2)+'. '\n",
        "    p.append(text)\n",
        "    # text = str(row.P2)+' has_ppi_interaction '+ str(row.P1)+'. '\n",
        "    # p.append(text)\n",
        "ppi=\"\"\n",
        "for c in p:\n",
        "  ppi= ppi+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R10NK01ZILsX"
      },
      "outputs": [],
      "source": [
        "GRN = pd.read_csv(\"GRN-extracted scores more than 90-cleaned.txt\")\n",
        "G=[]\n",
        "for idx, row in GRN.iterrows():\n",
        "  if row.G1 in gensymbol and row.G2 in gensymbol:\n",
        "    text = str(row.G1)+' has_GRN_interaction '+ str(row.G2)+'. '\n",
        "    G.append(text)\n",
        "    # text = str(row.G2)+' has_GRN_interaction '+ str(row.G1)+'. '\n",
        "    # G.append(text)\n",
        "grn=\"\"\n",
        "for c in G:\n",
        "  grn= grn+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uChMRBszaTLw"
      },
      "outputs": [],
      "source": [
        "# has_domain relation. data is collected using UniProt\n",
        "D=[]\n",
        "dom= pd.read_csv(\"my_domain.txt\")\n",
        "dom.set_index(\"Unnamed: 0\", inplace=True)\n",
        "for idx, row in dom.iterrows():\n",
        "  for c in dom.columns:\n",
        "    if row[c] ==1 :\n",
        "      text =str(idx)+\" has_domain \"+str(c)+\". \"\n",
        "      D.append(text)\n",
        "\n",
        "domain=\"\"\n",
        "for c in D:\n",
        "  domain= domain+c\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0EGF5zFsSkTE"
      },
      "outputs": [],
      "source": [
        "# has_chemical relation. data is collected using Pubchem\n",
        "C=[]\n",
        "finger= pd.read_csv(\"my_finger.txt\")\n",
        "finger.set_index(\"Unnamed: 0\", inplace=True)\n",
        "for idx, row in finger.iterrows():\n",
        "  for c in finger.columns:\n",
        "    if row[c] ==1 :\n",
        "      text =str(idx)+\" has_chemical S\"+str(c)+\". \"\n",
        "      C.append(text)\n",
        "chemical=\"\"\n",
        "for c in C:\n",
        "  chemical= chemical+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ZSOlKfUOBJA"
      },
      "outputs": [],
      "source": [
        "# has_atc_code relation. data is collected using Sider4.1\n",
        "\n",
        "ATC=[]\n",
        "atc= pd.read_csv(\"my_ATC.txt\")\n",
        "atc.set_index(\"Unnamed: 0\", inplace=True)\n",
        "for idx, row in atc.iterrows():\n",
        "  for c in atc.columns:\n",
        "    if row[c] == 1 and (len(c)==4 or len(c)==1 or len(c)==3 or len(c)==5): # we consider first 4 level of ATC code\n",
        "      text =str(idx)+\" has_atc_code \"+str(c)+\". \"\n",
        "      ATC.append(text)\n",
        "atccode=\"\"\n",
        "for c in ATC:\n",
        "  atccode= atccode+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "usc_IO_5YdZh"
      },
      "outputs": [],
      "source": [
        "# has_sideeffect relation. Date is collected using Sider4.1\n",
        "S=[]\n",
        "side= pd.read_csv(\"my_sideeffect.txt\")\n",
        "side.set_index(\"Unnamed: 0\", inplace=True)\n",
        "for idx, row in side.iterrows():\n",
        "  for c in side.columns:\n",
        "    if row[c] ==1 :\n",
        "      text =str(idx)+\" has_sideeffect \"+str(c)+\". \"\n",
        "      S.append(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aATmoM4APuWX"
      },
      "outputs": [],
      "source": [
        "# target-domain relation. Date is collected using target_domain_association\n",
        "TD=[]\n",
        "td= pd.read_csv(\"target_domain_association.txt\")\n",
        "td\n",
        "td.set_index(\"Unnamed: 0\", inplace=True)\n",
        "td\n",
        "for idx, row in td.iterrows():\n",
        "  if idx in target.columns:\n",
        "    for c in td.columns:\n",
        "      if row[c] ==1 and c in dom.columns:\n",
        "        text =str(idx)+\" has_tardom \"+str(c)+\". \"\n",
        "        TD.append(text)\n",
        "\n",
        "tardom=\"\"\n",
        "for c in TD:\n",
        "  tardom= tardom+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rlHVqIMdGi9k"
      },
      "outputs": [],
      "source": [
        "# target-gene relation. Date is collected using DisGenet\n",
        "TG=[]\n",
        "tg= pd.read_csv(\"Uniprot-2-GeneID.csv\")\n",
        "tg\n",
        "\n",
        "for idx, row in tg.iterrows():\n",
        "  if row.UniProtKB in target.columns:\n",
        "    if row.GENEID in genid.keys():\n",
        "      text =str(row.UniProtKB)+\" has_targen \"+str(row.GENEID)+\". \"\n",
        "      TG.append(text)\n",
        "\n",
        "targene=\"\"\n",
        "for c in TG:\n",
        "  targene= targene+c"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "kRXTA448ULst"
      },
      "source": [
        "Extracting drug-disease associations\n",
        "\n",
        "Positive data: Known drug-disease associations\n",
        "\n",
        "Negative data: a subset of unknown drug-disease associations that disease is an adverse drug reaction and common with our side effects\n",
        "\n",
        "More over I did it 10 times for getting the results of 10-fold cross validation based on positive data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZtriJmeR2mZw"
      },
      "outputs": [],
      "source": [
        "A_test=[]\n",
        "i=0\n",
        "# randomly chose 10% (78) of positive data for test set\n",
        "# then extract those pairs from the documnet of associations that word2vec learns\n",
        "while(i!=74):\n",
        "  a1 = (random.sample(A, 1))\n",
        "  a = a1[0].split(\" \")\n",
        "  a2=a[2].split(\".\")\n",
        "  A_test.append(a1[0])\n",
        "  A.remove(a1[0]) # removing the drug-disease pairs that we aim to check their associations by the model\n",
        "  i+=1\n",
        "\n",
        "ass=\"\" # making the new associations for training the models\n",
        "for c in A:\n",
        "  ass= ass+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNBl2eMK2mZ1",
        "outputId": "db820491-23e1-4ca7-d185-38447fc6676e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The number of  known drug disease associations without positive test set: (674, 2)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "I=[]\n",
        "for idx, row in has.iterrows():\n",
        "  text=str(row.drug_id)+\" has_indicated \"+str(row.ind_id)+\". \"\n",
        "  if text in A_test: # Extracting test associations from positive data\n",
        "    I.append(idx)\n",
        "has = has.drop(index=I, axis=0)\n",
        "print(\"The number of  known drug disease associations without positive test set:\",has.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fjOm6_gAR9Qp",
        "outputId": "5d13e73e-aab8-41b4-da96-0cc4ea1eac6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The number of unknown drug disease associations in our dataset:  (1966, 2)\n",
            "The number of unknown drug disease associations without negative test set:  (1888, 2)\n"
          ]
        }
      ],
      "source": [
        "# hasnt = hasnt.rename(columns={\"Not_indicated_for\":\"ind_id\", \"Drug_id\": \"drug_id\"})\n",
        "\n",
        "print(\"The number of unknown drug disease associations in our dataset: \",(hasnt.shape))\n",
        "\n",
        "I=[]\n",
        "u=0\n",
        "hasnt_test_idx=random.sample(range(0,hasnt.shape[0]), 78)\n",
        "for idx, row in hasnt.iterrows():\n",
        "  if idx in hasnt_test_idx:\n",
        "    I.append(row)\n",
        "    x = str(row.drug_id)+\" has_sideeffect \"+str(row.ind_id)+\". \"  # we extract those has_sideeffect relations from drug which are in negative test set and we consider them as adverse drug reactions\n",
        "    if(x in S):\n",
        "      S.remove(x)\n",
        "      u = u+1\n",
        "hasnt = hasnt.drop(index=hasnt_test_idx, axis=0)\n",
        "print(\"The number of unknown drug disease associations without negative test set: \",(hasnt.shape))\n",
        "\n",
        "side=\"\"\n",
        "for c in S:\n",
        "  side= side+c\n",
        "I=pd.DataFrame(I)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "4ocxfgkg4f73"
      },
      "source": [
        "#Word2Vec implementation\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "HpG5IkfTtXcq"
      },
      "source": [
        "first, we make the file of our DDKG\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-x6g4u2abgg6"
      },
      "outputs": [],
      "source": [
        "\n",
        "data=[]\n",
        "sets=[chemical, target1, side, domain, atccode, tardom, targene, grn, ppi, ass, Disease] #Main\n",
        "# sets=[chemical, target1, side, domain, atccode, tardom, ppi,grn, ass, Disease] #Internal\n",
        "# sets=[chemical, target1, side, domain, atccode, ass, Disease] #Basic\n",
        "# sets=[chemical, target1, side, domain, atccode, targene, ass, Disease] #External\n",
        "\n",
        "\n",
        "for s in sets:\n",
        "  for i in sent_tokenize(s):\n",
        "      temp = []\n",
        "      # tokenize the sentence into words\n",
        "      for j in word_tokenize(i):\n",
        "          temp.append(j.lower())\n",
        "      data.append(temp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YPCZWjv5hiDH",
        "outputId": "304364d7-dfc9-434b-e7a1-efa522a1ecf7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "12812"
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "word_freq = defaultdict(int)\n",
        "for sent in data:\n",
        "    for i in sent:\n",
        "        word_freq[i] += 1\n",
        "len(word_freq)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "L0gAjZPutovy"
      },
      "source": [
        "we train Word2Vec-CBOW for embedding every entity of DDKG including diseases and drugs\n",
        "\n",
        "based on trial and error, the best performance of Word2Vec on our dataset is when the length of vectors (size) is 700 and the iteration (iter) is 2000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "5D8HsO0XdIWo",
        "outputId": "f57da9f4-532a-4893-89bc-95aae26f39cb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:gensim.models.keyedvectors:destructive init_sims(replace=True) deprecated & no longer required for space-efficiency\n"
          ]
        }
      ],
      "source": [
        "model1 = gensim.models.Word2Vec(min_count = 1, alpha=0.001, vector_size = 1024, window = 2, epochs=100, sg=0)\n",
        "model1.build_vocab(data, progress_per=1000)\n",
        "model1.train(data, total_examples=model1.corpus_count, epochs=500, report_delay=1)\n",
        "model1.init_sims(replace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "qqhNP4nj_dUD"
      },
      "outputs": [],
      "source": [
        "def mean_cal(K):\n",
        "  sum=0\n",
        "  for k in K:\n",
        "    sum= sum+k\n",
        "  return(sum/len(K))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "UzqR5mySieAZ"
      },
      "outputs": [],
      "source": [
        "def model_history(history):\n",
        "  plt.plot(history.history['accuracy'])\n",
        "  plt.plot(history.history['val_accuracy'])\n",
        "  plt.title('model accuracy')\n",
        "  plt.ylabel('accuracy')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'test'], loc='upper left')\n",
        "  plt.show()\n",
        "  # summarize history for loss\n",
        "  plt.plot(history.history['loss'])\n",
        "  plt.plot(history.history['val_loss'])\n",
        "  plt.title('model loss')\n",
        "  plt.ylabel('loss')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'test'], loc='upper left')\n",
        "  plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "9_O6CwblyN-E"
      },
      "source": [
        "# Implementation\n",
        "\n",
        "we consider several approaches:\n",
        "1. unbalanced dataset\n",
        "2. Undersampling negative data\n",
        "3. Upsampling positive data (Random oversample and SMOTE techniqe)\n",
        "4. Upsampling positive data and undersampling negative data"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "BZ-67a8zhrsX"
      },
      "source": [
        "## Unbalanced"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "chcfRe4LWFfJ"
      },
      "outputs": [],
      "source": [
        "frames = [has, hasnt]\n",
        "f = pd.concat(frames)\n",
        "X=[]\n",
        "for idx, row in f.iterrows():\n",
        "  X.append(np.concatenate((model1.wv[(row.drug_id).lower()].reshape((1,1024)), model1.wv[(row.ind_id).lower()].reshape((1,1024))), axis=None))\n",
        "\n",
        "\n",
        "y=np.zeros((len(X), ))\n",
        "\n",
        "for i in range(has.shape[0]):\n",
        "  y[i]=1\n",
        "\n",
        "# the main test set\n",
        "X_me = []\n",
        "Y_me = []\n",
        "for a in A_test:\n",
        "  sample = a.split(\" \")\n",
        "  dname =sample[0]\n",
        "  disname = sample[2].split(\".\")\n",
        "  X_me.append(np.concatenate((model1.wv[(dname.rstrip().lower())].reshape((1, 1024)), model1.wv[(disname[0].lstrip().lower())].reshape((1, 1024))), axis=None))\n",
        "  Y_me.append(1)\n",
        "for idx, i in I.iterrows():\n",
        "  if(i.ind_id in my_disease_list and i.drug_id in my_drug_list):\n",
        "    X_me.append(np.concatenate((model1.wv[i.drug_id.lower()].reshape((1, 1024)), model1.wv[i.ind_id.lower()].reshape((1, 1024))), axis=None))\n",
        "    Y_me.append(0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "0PdMAKlQWFfN",
        "outputId": "237a6b50-2dc4-463a-9910-84e46dbb6870"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "61/61 [==============================] - 4s 11ms/step - loss: 0.6422 - accuracy: 0.7319 - val_loss: 0.5694 - val_accuracy: 0.7441 - lr: 5.0000e-05\n",
            "Epoch 2/100\n",
            "61/61 [==============================] - 0s 7ms/step - loss: 0.5513 - accuracy: 0.7345 - val_loss: 0.5055 - val_accuracy: 0.7441 - lr: 5.0000e-05\n",
            "Epoch 3/100\n",
            "61/61 [==============================] - 0s 7ms/step - loss: 0.4929 - accuracy: 0.7350 - val_loss: 0.4522 - val_accuracy: 0.7441 - lr: 5.0000e-05\n",
            "Epoch 4/100\n",
            "61/61 [==============================] - 1s 8ms/step - loss: 0.4468 - accuracy: 0.7460 - val_loss: 0.4188 - val_accuracy: 0.8034 - lr: 5.0000e-05\n",
            "Epoch 5/100\n",
            "61/61 [==============================] - 1s 9ms/step - loss: 0.4115 - accuracy: 0.8037 - val_loss: 0.3920 - val_accuracy: 0.8284 - lr: 5.0000e-05\n",
            "Epoch 6/100\n",
            "61/61 [==============================] - 1s 10ms/step - loss: 0.3910 - accuracy: 0.8246 - val_loss: 0.3709 - val_accuracy: 0.8456 - lr: 5.0000e-05\n",
            "Epoch 7/100\n",
            "61/61 [==============================] - 1s 12ms/step - loss: 0.3684 - accuracy: 0.8496 - val_loss: 0.3562 - val_accuracy: 0.8565 - lr: 5.0000e-05\n",
            "Epoch 8/100\n",
            "61/61 [==============================] - 1s 13ms/step - loss: 0.3538 - accuracy: 0.8485 - val_loss: 0.3498 - val_accuracy: 0.8612 - lr: 5.0000e-05\n",
            "Epoch 9/100\n",
            "61/61 [==============================] - 1s 10ms/step - loss: 0.3438 - accuracy: 0.8558 - val_loss: 0.3418 - val_accuracy: 0.8580 - lr: 5.0000e-05\n",
            "Epoch 10/100\n",
            "61/61 [==============================] - 0s 8ms/step - loss: 0.3390 - accuracy: 0.8589 - val_loss: 0.3515 - val_accuracy: 0.8565 - lr: 5.0000e-05\n",
            "Epoch 11/100\n",
            "54/61 [=========================>....] - ETA: 0s - loss: 0.3322 - accuracy: 0.8582\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-06.\n",
            "61/61 [==============================] - 0s 7ms/step - loss: 0.3321 - accuracy: 0.8594 - val_loss: 0.3396 - val_accuracy: 0.8565 - lr: 5.0000e-05\n",
            "Epoch 12/100\n",
            "61/61 [==============================] - 0s 7ms/step - loss: 0.3241 - accuracy: 0.8647 - val_loss: 0.3391 - val_accuracy: 0.8580 - lr: 5.0000e-06\n",
            "Epoch 13/100\n",
            "61/61 [==============================] - 0s 7ms/step - loss: 0.3334 - accuracy: 0.8532 - val_loss: 0.3387 - val_accuracy: 0.8580 - lr: 5.0000e-06\n",
            "Epoch 14/100\n",
            "59/61 [============================>.] - ETA: 0s - loss: 0.3206 - accuracy: 0.8655\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-07.\n",
            "61/61 [==============================] - 0s 7ms/step - loss: 0.3199 - accuracy: 0.8657 - val_loss: 0.3384 - val_accuracy: 0.8612 - lr: 5.0000e-06\n",
            "Epoch 15/100\n",
            "61/61 [==============================] - 0s 7ms/step - loss: 0.3230 - accuracy: 0.8636 - val_loss: 0.3384 - val_accuracy: 0.8612 - lr: 5.0000e-07\n",
            "Epoch 16/100\n",
            "61/61 [==============================] - 0s 7ms/step - loss: 0.3263 - accuracy: 0.8647 - val_loss: 0.3384 - val_accuracy: 0.8596 - lr: 5.0000e-07\n",
            "Epoch 17/100\n",
            "60/61 [============================>.] - ETA: 0s - loss: 0.3248 - accuracy: 0.8630Restoring model weights from the end of the best epoch: 8.\n",
            "\n",
            "Epoch 17: ReduceLROnPlateau reducing learning rate to 4.999999987376214e-08.\n",
            "61/61 [==============================] - 0s 7ms/step - loss: 0.3247 - accuracy: 0.8631 - val_loss: 0.3384 - val_accuracy: 0.8565 - lr: 5.0000e-07\n",
            "Epoch 17: early stopping\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Define the callbacks\n",
        "early_stopping = EarlyStopping(monitor='val_accuracy', patience=9, verbose=1, restore_best_weights=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_accuracy', factor=0.1, patience=3, verbose=1)\n",
        "model_checkpoint = ModelCheckpoint('best_weights.h5', monitor='val_accuracy', save_best_only=True)\n",
        "\n",
        "input_shape1 = (1024,)\n",
        "input_shape2 = (1024,)\n",
        "\n",
        "# Define the inputs for the two branches of the network\n",
        "input1 = Input(shape=input_shape1)\n",
        "input2 = Input(shape=input_shape2)\n",
        "\n",
        "# Define the layers for the first branch of the network\n",
        "x1 = Dense(512, activation='relu')(input1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(256, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(128, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(64, activation='relu')(x1)\n",
        "output1 = x1\n",
        "modeldrug = Model(input1, output1)\n",
        "modeldrug.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Define the layers for the second branch of the network\n",
        "x2 = Dense(512, activation='relu')(input2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(256, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(128, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(64, activation='relu')(x2)\n",
        "output2 = x2\n",
        "modeldisease = Model(input2, output2)\n",
        "modeldisease.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Subtract the embeddings from the two branches\n",
        "diff = Subtract()([output1, output2])\n",
        "diff = Lambda(lambda d: abs(d))(diff)\n",
        "# diff = Dropout(0.2)(diff)\n",
        "concat = Dense(32, activation= 'relu')(diff)\n",
        "# concat = Dropout(rate=0.2)(concat)\n",
        "\n",
        "\n",
        "# Define the output layer\n",
        "output = Dense(1, activation='sigmoid')(concat)\n",
        "\n",
        "# Define the model\n",
        "simmodel = Model(inputs=[input1, input2], outputs=output)\n",
        "\n",
        "# Define the optimizer and compile the model\n",
        "optimizer = Adam(learning_rate=0.00005)\n",
        "simmodel.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "\n",
        "# # Define the class weights\n",
        "class_weights = {0: 1, 1: 1966/748 } # inverse frequency weighting\n",
        "\n",
        "\n",
        "# Train the model on your data with the defined callbacks\n",
        "\n",
        "X_me = np.array(X_me)\n",
        "Y_me = np.array(Y_me)\n",
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "l_v = np.array(X_me[:, :1024])\n",
        "r_v = np.array(X_me[:, 1024:])\n",
        "\n",
        "x_train, x_val, y_train, y_val = train_test_split(X, y)\n",
        "\n",
        "l = np.array(x_train[:, :1024])\n",
        "r = np.array(x_train[:, 1024:])\n",
        "\n",
        "\n",
        "l_val = np.array(x_val[:, :1024])\n",
        "r_val = np.array(x_val[:, 1024:])\n",
        "\n",
        "history = simmodel.fit([l, r], y_train, batch_size=32, epochs=100,validation_data=([l_val, r_val], y_val), callbacks=[early_stopping, reduce_lr, model_checkpoint])\n",
        "\n",
        "# history = simmodel.fit([l, r], y_train, batch_size= 32, epochs= 300, class_weight=class_weights, validation_data=([l_val, r_val],y_val)\n",
        "                  # ,callbacks=[callback]\n",
        "                  # )\n",
        "model_history(history)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "mZKRxmhbk-CS",
        "outputId": "8f5ae06d-f3a6-4e42-b5d0-ce9f9022f740"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5/5 [==============================] - 0s 4ms/step - loss: 0.3970 - accuracy: 0.8092\n",
            "[0.39703965187072754, 0.8092105388641357]\n",
            "5/5 [==============================] - 0s 3ms/step\n",
            "auc-roc =  0.9275814275814276\n",
            "auc-pr =  0.8843650898200224\n",
            "Brier score: 0.12406749602315094\n",
            "Cohen Kapp score: 0.6158940397350994\n",
            "MCC score: 0.632126727471794\n"
          ]
        }
      ],
      "source": [
        "print(simmodel.evaluate([l_v, r_v], Y_me))\n",
        "y_hat= simmodel.predict([l_v, r_v])\n",
        "A = roc_auc_score(Y_me, y_hat)\n",
        "print(\"auc-roc = \", A)\n",
        "\n",
        "precision, recall, thresholds = precision_recall_curve(Y_me, y_hat)\n",
        "PR = auc(recall, precision)\n",
        "\n",
        "print(\"auc-pr = \", PR)\n",
        "\n",
        "print(\"Brier score:\", brier_score_loss(Y_me, y_hat))\n",
        "\n",
        "y_hat_e = []\n",
        "for item in y_hat:\n",
        "  if item >= 0.5:\n",
        "    y_hat_e.append(1)\n",
        "  else:\n",
        "    y_hat_e.append(0)\n",
        "print(\"Cohen Kapp score:\", cohen_kappa_score(Y_me, y_hat_e))\n",
        "print(\"MCC score:\", matthews_corrcoef(Y_me, y_hat_e))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "wpaDRVRzjrih"
      },
      "source": [
        "##  Undersample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "3zQ2pmM7jvcy",
        "outputId": "b3982fa9-e1ed-442a-abb6-470067141b70"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1348\n"
          ]
        }
      ],
      "source": [
        "frames = [has, hasnt]\n",
        "f = pd.concat(frames)\n",
        "X=[]\n",
        "for idx, row in f.iterrows():\n",
        "  X.append(np.concatenate((model1.wv[(row.drug_id).lower()].reshape((1,1024)), model1.wv[(row.ind_id).lower()].reshape((1,1024))), axis=None))\n",
        "\n",
        "\n",
        "y=np.zeros((len(X), ))\n",
        "\n",
        "for i in range(has.shape[0]):\n",
        "  y[i]=1\n",
        "\n",
        "# the main test set\n",
        "X_me = []\n",
        "Y_me = []\n",
        "for a in A_test:\n",
        "  sample = a.split(\" \")\n",
        "  dname =sample[0]\n",
        "  disname = sample[2].split(\".\")\n",
        "  X_me.append(np.concatenate((model1.wv[(dname.rstrip().lower())].reshape((1, 1024)), model1.wv[(disname[0].lstrip().lower())].reshape((1, 1024))), axis=None))\n",
        "  Y_me.append(1)\n",
        "for idx, i in I.iterrows():\n",
        "  if(i.ind_id in my_disease_list and i.drug_id in my_drug_list):\n",
        "    X_me.append(np.concatenate((model1.wv[i.drug_id.lower()].reshape((1, 1024)), model1.wv[i.ind_id.lower()].reshape((1, 1024))), axis=None))\n",
        "    Y_me.append(0)\n",
        "\n",
        "\n",
        "under = RandomUnderSampler()\n",
        "X,y = under.fit_resample(X,y)\n",
        "print(len(X))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "qdNt8C4njvcz",
        "outputId": "6db6da06-f9df-4a1b-8b06-c88123190447"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "32/32 [==============================] - 4s 18ms/step - loss: 0.6904 - accuracy: 0.6053 - val_loss: 0.6841 - val_accuracy: 0.7507 - lr: 5.0000e-05\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 0s 7ms/step - loss: 0.6805 - accuracy: 0.7250 - val_loss: 0.6678 - val_accuracy: 0.7478 - lr: 5.0000e-05\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 0s 11ms/step - loss: 0.6599 - accuracy: 0.7735 - val_loss: 0.6389 - val_accuracy: 0.7626 - lr: 5.0000e-05\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 0s 12ms/step - loss: 0.6259 - accuracy: 0.7804 - val_loss: 0.5970 - val_accuracy: 0.8042 - lr: 5.0000e-05\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 0s 11ms/step - loss: 0.5773 - accuracy: 0.7943 - val_loss: 0.5464 - val_accuracy: 0.8131 - lr: 5.0000e-05\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 0s 10ms/step - loss: 0.5273 - accuracy: 0.7903 - val_loss: 0.4884 - val_accuracy: 0.8190 - lr: 5.0000e-05\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 0s 8ms/step - loss: 0.4796 - accuracy: 0.8091 - val_loss: 0.4485 - val_accuracy: 0.8131 - lr: 5.0000e-05\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 0s 12ms/step - loss: 0.4520 - accuracy: 0.8111 - val_loss: 0.4235 - val_accuracy: 0.8220 - lr: 5.0000e-05\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 0s 8ms/step - loss: 0.4267 - accuracy: 0.8180 - val_loss: 0.4109 - val_accuracy: 0.8160 - lr: 5.0000e-05\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 0s 7ms/step - loss: 0.4196 - accuracy: 0.8140 - val_loss: 0.4398 - val_accuracy: 0.8012 - lr: 5.0000e-05\n",
            "Epoch 11/100\n",
            "31/32 [============================>.] - ETA: 0s - loss: 0.4176 - accuracy: 0.8165\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-06.\n",
            "32/32 [==============================] - 0s 10ms/step - loss: 0.4200 - accuracy: 0.8140 - val_loss: 0.4117 - val_accuracy: 0.8160 - lr: 5.0000e-05\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 1s 16ms/step - loss: 0.4010 - accuracy: 0.8289 - val_loss: 0.4010 - val_accuracy: 0.8279 - lr: 5.0000e-06\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 0s 15ms/step - loss: 0.4039 - accuracy: 0.8210 - val_loss: 0.3969 - val_accuracy: 0.8338 - lr: 5.0000e-06\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 1s 17ms/step - loss: 0.4022 - accuracy: 0.8229 - val_loss: 0.3959 - val_accuracy: 0.8368 - lr: 5.0000e-06\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 0s 11ms/step - loss: 0.3985 - accuracy: 0.8259 - val_loss: 0.3957 - val_accuracy: 0.8368 - lr: 5.0000e-06\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 0s 11ms/step - loss: 0.4010 - accuracy: 0.8348 - val_loss: 0.3941 - val_accuracy: 0.8368 - lr: 5.0000e-06\n",
            "Epoch 17/100\n",
            "31/32 [============================>.] - ETA: 0s - loss: 0.3992 - accuracy: 0.8256\n",
            "Epoch 17: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-07.\n",
            "32/32 [==============================] - 0s 12ms/step - loss: 0.4027 - accuracy: 0.8239 - val_loss: 0.3951 - val_accuracy: 0.8368 - lr: 5.0000e-06\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 0s 8ms/step - loss: 0.4024 - accuracy: 0.8279 - val_loss: 0.3953 - val_accuracy: 0.8368 - lr: 5.0000e-07\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 0s 9ms/step - loss: 0.3997 - accuracy: 0.8269 - val_loss: 0.3950 - val_accuracy: 0.8368 - lr: 5.0000e-07\n",
            "Epoch 20/100\n",
            "27/32 [========================>.....] - ETA: 0s - loss: 0.3947 - accuracy: 0.8241\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 4.999999987376214e-08.\n",
            "32/32 [==============================] - 0s 8ms/step - loss: 0.3941 - accuracy: 0.8269 - val_loss: 0.3949 - val_accuracy: 0.8368 - lr: 5.0000e-07\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 0s 8ms/step - loss: 0.3992 - accuracy: 0.8279 - val_loss: 0.3949 - val_accuracy: 0.8368 - lr: 5.0000e-08\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 0s 9ms/step - loss: 0.4063 - accuracy: 0.8328 - val_loss: 0.3949 - val_accuracy: 0.8368 - lr: 5.0000e-08\n",
            "Epoch 23/100\n",
            "28/32 [=========================>....] - ETA: 0s - loss: 0.4019 - accuracy: 0.8281Restoring model weights from the end of the best epoch: 14.\n",
            "\n",
            "Epoch 23: ReduceLROnPlateau reducing learning rate to 5.000000058430488e-09.\n",
            "32/32 [==============================] - 0s 8ms/step - loss: 0.4021 - accuracy: 0.8289 - val_loss: 0.3949 - val_accuracy: 0.8368 - lr: 5.0000e-08\n",
            "Epoch 23: early stopping\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Define the callbacks\n",
        "early_stopping = EarlyStopping(monitor='val_accuracy', patience=9, verbose=1, restore_best_weights=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_accuracy', factor=0.1, patience=3, verbose=1)\n",
        "model_checkpoint = ModelCheckpoint('best_weights.h5', monitor='val_accuracy', save_best_only=True)\n",
        "\n",
        "input_shape1 = (1024,)\n",
        "input_shape2 = (1024,)\n",
        "\n",
        "# Define the inputs for the two branches of the network\n",
        "input1 = Input(shape=input_shape1)\n",
        "input2 = Input(shape=input_shape2)\n",
        "\n",
        "# Define the layers for the first branch of the network\n",
        "x1 = Dense(512, activation='relu')(input1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(256, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(128, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(64, activation='relu')(x1)\n",
        "output1 = x1\n",
        "modeldrug = Model(input1, output1)\n",
        "modeldrug.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Define the layers for the second branch of the network\n",
        "x2 = Dense(512, activation='relu')(input2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(256, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(128, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(64, activation='relu')(x2)\n",
        "output2 = x2\n",
        "modeldisease = Model(input2, output2)\n",
        "modeldisease.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Subtract the embeddings from the two branches\n",
        "diff = Subtract()([output1, output2])\n",
        "diff = Lambda(lambda d: abs(d))(diff)\n",
        "# diff = Dropout(0.2)(diff)\n",
        "concat = Dense(32, activation= 'relu')(diff)\n",
        "# concat = Dropout(rate=0.2)(concat)\n",
        "\n",
        "\n",
        "# Define the output layer\n",
        "output = Dense(1, activation='sigmoid')(concat)\n",
        "\n",
        "# Define the model\n",
        "simmodel = Model(inputs=[input1, input2], outputs=output)\n",
        "\n",
        "# Define the optimizer and compile the model\n",
        "optimizer = Adam(learning_rate=0.00005)\n",
        "simmodel.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "\n",
        "# # Define the class weights\n",
        "class_weights = {0: 1, 1: 1966/748 } # inverse frequency weighting\n",
        "\n",
        "\n",
        "# Train the model on your data with the defined callbacks\n",
        "\n",
        "X_me = np.array(X_me)\n",
        "Y_me = np.array(Y_me)\n",
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "l_v = np.array(X_me[:, :1024])\n",
        "r_v = np.array(X_me[:, 1024:])\n",
        "\n",
        "x_train, x_val, y_train, y_val = train_test_split(X, y)\n",
        "\n",
        "l = np.array(x_train[:, :1024])\n",
        "r = np.array(x_train[:, 1024:])\n",
        "\n",
        "\n",
        "l_val = np.array(x_val[:, :1024])\n",
        "r_val = np.array(x_val[:, 1024:])\n",
        "\n",
        "history = simmodel.fit([l, r], y_train, batch_size=32, epochs=100,validation_data=([l_val, r_val], y_val), callbacks=[early_stopping, reduce_lr, model_checkpoint])\n",
        "\n",
        "# history = simmodel.fit([l, r], y_train, batch_size= 32, epochs= 300, class_weight=class_weights, validation_data=([l_val, r_val],y_val)\n",
        "                  # ,callbacks=[callback]\n",
        "                  # )\n",
        "model_history(history)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "kNi2KQQalAeN",
        "outputId": "ecec2854-637d-4b5f-a206-27f330f198d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5/5 [==============================] - 0s 5ms/step - loss: 0.3405 - accuracy: 0.8618\n",
            "[0.3404821455478668, 0.8618420958518982]\n",
            "5/5 [==============================] - 0s 3ms/step\n",
            "auc-roc =  0.9331254331254331\n",
            "auc-pr =  0.9256623743767484\n",
            "Brier score: 0.10376330152805462\n",
            "Cohen Kapp score: 0.7243523316062177\n",
            "MCC score: 0.7294603209022543\n"
          ]
        }
      ],
      "source": [
        "print(simmodel.evaluate([l_v, r_v], Y_me))\n",
        "y_hat= simmodel.predict([l_v, r_v])\n",
        "A = roc_auc_score(Y_me, y_hat)\n",
        "print(\"auc-roc = \", A)\n",
        "\n",
        "precision, recall, thresholds = precision_recall_curve(Y_me, y_hat)\n",
        "PR = auc(recall, precision)\n",
        "\n",
        "print(\"auc-pr = \", PR)\n",
        "\n",
        "print(\"Brier score:\", brier_score_loss(Y_me, y_hat))\n",
        "\n",
        "y_hat_e = []\n",
        "for item in y_hat:\n",
        "  if item >= 0.5:\n",
        "    y_hat_e.append(1)\n",
        "  else:\n",
        "    y_hat_e.append(0)\n",
        "print(\"Cohen Kapp score:\", cohen_kappa_score(Y_me, y_hat_e))\n",
        "print(\"MCC score:\", matthews_corrcoef(Y_me, y_hat_e))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "8jG8n8m-kFwk"
      },
      "source": [
        "##  Oversample Using Random method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "U5bKlFQUkIPE",
        "outputId": "e478b957-f8c9-44c7-e065-e913878ab403"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3587\n"
          ]
        }
      ],
      "source": [
        "frames = [has, hasnt]\n",
        "f = pd.concat(frames)\n",
        "X=[]\n",
        "for idx, row in f.iterrows():\n",
        "  X.append(np.concatenate((model1.wv[(row.drug_id).lower()].reshape((1,1024)), model1.wv[(row.ind_id).lower()].reshape((1,1024))), axis=None))\n",
        "\n",
        "\n",
        "y=np.zeros((len(X), ))\n",
        "\n",
        "for i in range(has.shape[0]):\n",
        "  y[i]=1\n",
        "\n",
        "# the main test set\n",
        "X_me = []\n",
        "Y_me = []\n",
        "for a in A_test:\n",
        "  sample = a.split(\" \")\n",
        "  dname =sample[0]\n",
        "  disname = sample[2].split(\".\")\n",
        "  X_me.append(np.concatenate((model1.wv[(dname.rstrip().lower())].reshape((1, 1024)), model1.wv[(disname[0].lstrip().lower())].reshape((1, 1024))), axis=None))\n",
        "  Y_me.append(1)\n",
        "for idx, i in I.iterrows():\n",
        "  if(i.ind_id in my_disease_list and i.drug_id in my_drug_list):\n",
        "    X_me.append(np.concatenate((model1.wv[i.drug_id.lower()].reshape((1, 1024)), model1.wv[i.ind_id.lower()].reshape((1, 1024))), axis=None))\n",
        "    Y_me.append(0)\n",
        "\n",
        "over = RandomOverSampler(sampling_strategy=0.9)\n",
        "X, y = over.fit_resample(X,y)\n",
        "print(len(X))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "B-84_rSdkIPG",
        "outputId": "1e5c50be-3acf-4893-9f4c-cd2135ee386d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "85/85 [==============================] - 4s 10ms/step - loss: 0.6808 - accuracy: 0.6175 - val_loss: 0.6472 - val_accuracy: 0.7726 - lr: 5.0000e-05\n",
            "Epoch 2/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.6043 - accuracy: 0.7677 - val_loss: 0.5132 - val_accuracy: 0.8194 - lr: 5.0000e-05\n",
            "Epoch 3/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.4871 - accuracy: 0.7933 - val_loss: 0.4243 - val_accuracy: 0.8227 - lr: 5.0000e-05\n",
            "Epoch 4/100\n",
            "85/85 [==============================] - 1s 9ms/step - loss: 0.4333 - accuracy: 0.8086 - val_loss: 0.4000 - val_accuracy: 0.8328 - lr: 5.0000e-05\n",
            "Epoch 5/100\n",
            "85/85 [==============================] - 1s 10ms/step - loss: 0.4213 - accuracy: 0.8112 - val_loss: 0.3973 - val_accuracy: 0.8272 - lr: 5.0000e-05\n",
            "Epoch 6/100\n",
            "85/85 [==============================] - 1s 11ms/step - loss: 0.4050 - accuracy: 0.8197 - val_loss: 0.3824 - val_accuracy: 0.8372 - lr: 5.0000e-05\n",
            "Epoch 7/100\n",
            "85/85 [==============================] - 1s 9ms/step - loss: 0.4040 - accuracy: 0.8156 - val_loss: 0.3770 - val_accuracy: 0.8350 - lr: 5.0000e-05\n",
            "Epoch 8/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.3966 - accuracy: 0.8204 - val_loss: 0.3733 - val_accuracy: 0.8361 - lr: 5.0000e-05\n",
            "Epoch 9/100\n",
            "76/85 [=========================>....] - ETA: 0s - loss: 0.3903 - accuracy: 0.8265\n",
            "Epoch 9: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-06.\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3876 - accuracy: 0.8297 - val_loss: 0.3757 - val_accuracy: 0.8339 - lr: 5.0000e-05\n",
            "Epoch 10/100\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3845 - accuracy: 0.8275 - val_loss: 0.3720 - val_accuracy: 0.8372 - lr: 5.0000e-06\n",
            "Epoch 11/100\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3852 - accuracy: 0.8238 - val_loss: 0.3718 - val_accuracy: 0.8372 - lr: 5.0000e-06\n",
            "Epoch 12/100\n",
            "85/85 [==============================] - 1s 9ms/step - loss: 0.3874 - accuracy: 0.8268 - val_loss: 0.3712 - val_accuracy: 0.8395 - lr: 5.0000e-06\n",
            "Epoch 13/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.3825 - accuracy: 0.8257 - val_loss: 0.3697 - val_accuracy: 0.8372 - lr: 5.0000e-06\n",
            "Epoch 14/100\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3825 - accuracy: 0.8275 - val_loss: 0.3713 - val_accuracy: 0.8372 - lr: 5.0000e-06\n",
            "Epoch 15/100\n",
            "85/85 [==============================] - ETA: 0s - loss: 0.3811 - accuracy: 0.8294\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-07.\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3811 - accuracy: 0.8294 - val_loss: 0.3701 - val_accuracy: 0.8395 - lr: 5.0000e-06\n",
            "Epoch 16/100\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3825 - accuracy: 0.8260 - val_loss: 0.3698 - val_accuracy: 0.8384 - lr: 5.0000e-07\n",
            "Epoch 17/100\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3838 - accuracy: 0.8238 - val_loss: 0.3699 - val_accuracy: 0.8384 - lr: 5.0000e-07\n",
            "Epoch 18/100\n",
            "79/85 [==========================>...] - ETA: 0s - loss: 0.3793 - accuracy: 0.8307\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 4.999999987376214e-08.\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.3763 - accuracy: 0.8316 - val_loss: 0.3697 - val_accuracy: 0.8395 - lr: 5.0000e-07\n",
            "Epoch 19/100\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3828 - accuracy: 0.8271 - val_loss: 0.3697 - val_accuracy: 0.8395 - lr: 5.0000e-08\n",
            "Epoch 20/100\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3816 - accuracy: 0.8361 - val_loss: 0.3697 - val_accuracy: 0.8395 - lr: 5.0000e-08\n",
            "Epoch 21/100\n",
            "85/85 [==============================] - ETA: 0s - loss: 0.3853 - accuracy: 0.8245Restoring model weights from the end of the best epoch: 12.\n",
            "\n",
            "Epoch 21: ReduceLROnPlateau reducing learning rate to 5.000000058430488e-09.\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3853 - accuracy: 0.8245 - val_loss: 0.3697 - val_accuracy: 0.8384 - lr: 5.0000e-08\n",
            "Epoch 21: early stopping\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Define the callbacks\n",
        "early_stopping = EarlyStopping(monitor='val_accuracy', patience=9, verbose=1, restore_best_weights=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_accuracy', factor=0.1, patience=3, verbose=1)\n",
        "model_checkpoint = ModelCheckpoint('best_weights.h5', monitor='val_accuracy', save_best_only=True)\n",
        "\n",
        "input_shape1 = (1024,)\n",
        "input_shape2 = (1024,)\n",
        "\n",
        "# Define the inputs for the two branches of the network\n",
        "input1 = Input(shape=input_shape1)\n",
        "input2 = Input(shape=input_shape2)\n",
        "\n",
        "# Define the layers for the first branch of the network\n",
        "x1 = Dense(512, activation='relu')(input1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(256, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(128, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(64, activation='relu')(x1)\n",
        "output1 = x1\n",
        "modeldrug = Model(input1, output1)\n",
        "modeldrug.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Define the layers for the second branch of the network\n",
        "x2 = Dense(512, activation='relu')(input2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(256, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(128, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(64, activation='relu')(x2)\n",
        "output2 = x2\n",
        "modeldisease = Model(input2, output2)\n",
        "modeldisease.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Subtract the embeddings from the two branches\n",
        "diff = Subtract()([output1, output2])\n",
        "diff = Lambda(lambda d: abs(d))(diff)\n",
        "# diff = Dropout(0.2)(diff)\n",
        "concat = Dense(32, activation= 'relu')(diff)\n",
        "# concat = Dropout(rate=0.2)(concat)\n",
        "\n",
        "\n",
        "# Define the output layer\n",
        "output = Dense(1, activation='sigmoid')(concat)\n",
        "\n",
        "# Define the model\n",
        "simmodel = Model(inputs=[input1, input2], outputs=output)\n",
        "\n",
        "# Define the optimizer and compile the model\n",
        "optimizer = Adam(learning_rate=0.00005)\n",
        "simmodel.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "\n",
        "# # Define the class weights\n",
        "class_weights = {0: 1, 1: 1966/748 } # inverse frequency weighting\n",
        "\n",
        "\n",
        "# Train the model on your data with the defined callbacks\n",
        "\n",
        "X_me = np.array(X_me)\n",
        "Y_me = np.array(Y_me)\n",
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "l_v = np.array(X_me[:, :1024])\n",
        "r_v = np.array(X_me[:, 1024:])\n",
        "\n",
        "x_train, x_val, y_train, y_val = train_test_split(X, y)\n",
        "\n",
        "l = np.array(x_train[:, :1024])\n",
        "r = np.array(x_train[:, 1024:])\n",
        "\n",
        "\n",
        "l_val = np.array(x_val[:, :1024])\n",
        "r_val = np.array(x_val[:, 1024:])\n",
        "\n",
        "history = simmodel.fit([l, r], y_train, batch_size=32, epochs=100,validation_data=([l_val, r_val], y_val), callbacks=[early_stopping, reduce_lr, model_checkpoint])\n",
        "\n",
        "# history = simmodel.fit([l, r], y_train, batch_size= 32, epochs= 300, class_weight=class_weights, validation_data=([l_val, r_val],y_val)\n",
        "                  # ,callbacks=[callback]\n",
        "                  # )\n",
        "model_history(history)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "-SUvoedjlDMv",
        "outputId": "2cf03233-e6b1-47a0-dab3-910147cf6c40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5/5 [==============================] - 0s 6ms/step - loss: 0.3201 - accuracy: 0.8684\n",
            "[0.32012248039245605, 0.8684210777282715]\n",
            "5/5 [==============================] - 0s 3ms/step\n",
            "auc-roc =  0.9381496881496882\n",
            "auc-pr =  0.9182412407713727\n",
            "Brier score: 0.09602379660929158\n",
            "Cohen Kapp score: 0.7370242214532872\n",
            "MCC score: 0.738045738045738\n"
          ]
        }
      ],
      "source": [
        "print(simmodel.evaluate([l_v, r_v], Y_me))\n",
        "y_hat= simmodel.predict([l_v, r_v])\n",
        "A = roc_auc_score(Y_me, y_hat)\n",
        "print(\"auc-roc = \", A)\n",
        "\n",
        "precision, recall, thresholds = precision_recall_curve(Y_me, y_hat)\n",
        "PR = auc(recall, precision)\n",
        "\n",
        "print(\"auc-pr = \", PR)\n",
        "\n",
        "print(\"Brier score:\", brier_score_loss(Y_me, y_hat))\n",
        "\n",
        "y_hat_e = []\n",
        "for item in y_hat:\n",
        "  if item >= 0.5:\n",
        "    y_hat_e.append(1)\n",
        "  else:\n",
        "    y_hat_e.append(0)\n",
        "print(\"Cohen Kapp score:\", cohen_kappa_score(Y_me, y_hat_e))\n",
        "print(\"MCC score:\", matthews_corrcoef(Y_me, y_hat_e))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "AobYaPCjkYoL"
      },
      "source": [
        "##  oversample using SMOTE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "wyFuXc15kcUi",
        "outputId": "8f3e7ef9-8bec-4a66-9b33-82de237d7696"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3587\n"
          ]
        }
      ],
      "source": [
        "frames = [has, hasnt]\n",
        "f = pd.concat(frames)\n",
        "X=[]\n",
        "for idx, row in f.iterrows():\n",
        "  X.append(np.concatenate((model1.wv[(row.drug_id).lower()].reshape((1,1024)), model1.wv[(row.ind_id).lower()].reshape((1,1024))), axis=None))\n",
        "\n",
        "\n",
        "y=np.zeros((len(X), ))\n",
        "\n",
        "for i in range(has.shape[0]):\n",
        "  y[i]=1\n",
        "\n",
        "# the main test set\n",
        "X_me = []\n",
        "Y_me = []\n",
        "for a in A_test:\n",
        "  sample = a.split(\" \")\n",
        "  dname =sample[0]\n",
        "  disname = sample[2].split(\".\")\n",
        "  X_me.append(np.concatenate((model1.wv[(dname.rstrip().lower())].reshape((1, 1024)), model1.wv[(disname[0].lstrip().lower())].reshape((1, 1024))), axis=None))\n",
        "  Y_me.append(1)\n",
        "for idx, i in I.iterrows():\n",
        "  if(i.ind_id in my_disease_list and i.drug_id in my_drug_list):\n",
        "    X_me.append(np.concatenate((model1.wv[i.drug_id.lower()].reshape((1, 1024)), model1.wv[i.ind_id.lower()].reshape((1, 1024))), axis=None))\n",
        "    Y_me.append(0)\n",
        "\n",
        "\n",
        "over = SMOTE(sampling_strategy=0.9)\n",
        "# over = RandomOverSampler(0.9)\n",
        "X, y = over.fit_resample(X,y)\n",
        "print(len(X))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "FeRfqJxekcUk",
        "outputId": "1f39ec7b-9040-4a70-bf28-463a3864a51c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "85/85 [==============================] - 5s 11ms/step - loss: 0.6798 - accuracy: 0.7019 - val_loss: 0.6410 - val_accuracy: 0.8049 - lr: 5.0000e-05\n",
            "Epoch 2/100\n",
            "85/85 [==============================] - 1s 9ms/step - loss: 0.6006 - accuracy: 0.7944 - val_loss: 0.5084 - val_accuracy: 0.8239 - lr: 5.0000e-05\n",
            "Epoch 3/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.4871 - accuracy: 0.8011 - val_loss: 0.4238 - val_accuracy: 0.8239 - lr: 5.0000e-05\n",
            "Epoch 4/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.4318 - accuracy: 0.8141 - val_loss: 0.3872 - val_accuracy: 0.8339 - lr: 5.0000e-05\n",
            "Epoch 5/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.4163 - accuracy: 0.8130 - val_loss: 0.3862 - val_accuracy: 0.8384 - lr: 5.0000e-05\n",
            "Epoch 6/100\n",
            "85/85 [==============================] - 1s 9ms/step - loss: 0.4046 - accuracy: 0.8190 - val_loss: 0.3673 - val_accuracy: 0.8439 - lr: 5.0000e-05\n",
            "Epoch 7/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.4005 - accuracy: 0.8242 - val_loss: 0.3619 - val_accuracy: 0.8384 - lr: 5.0000e-05\n",
            "Epoch 8/100\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3968 - accuracy: 0.8283 - val_loss: 0.3578 - val_accuracy: 0.8406 - lr: 5.0000e-05\n",
            "Epoch 9/100\n",
            "82/85 [===========================>..] - ETA: 0s - loss: 0.3846 - accuracy: 0.8274\n",
            "Epoch 9: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-06.\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.3872 - accuracy: 0.8257 - val_loss: 0.3523 - val_accuracy: 0.8406 - lr: 5.0000e-05\n",
            "Epoch 10/100\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3806 - accuracy: 0.8283 - val_loss: 0.3518 - val_accuracy: 0.8439 - lr: 5.0000e-06\n",
            "Epoch 11/100\n",
            "85/85 [==============================] - 1s 9ms/step - loss: 0.3821 - accuracy: 0.8264 - val_loss: 0.3513 - val_accuracy: 0.8439 - lr: 5.0000e-06\n",
            "Epoch 12/100\n",
            "85/85 [==============================] - 1s 12ms/step - loss: 0.3881 - accuracy: 0.8227 - val_loss: 0.3508 - val_accuracy: 0.8462 - lr: 5.0000e-06\n",
            "Epoch 13/100\n",
            "85/85 [==============================] - 1s 10ms/step - loss: 0.3813 - accuracy: 0.8316 - val_loss: 0.3505 - val_accuracy: 0.8450 - lr: 5.0000e-06\n",
            "Epoch 14/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.3780 - accuracy: 0.8327 - val_loss: 0.3500 - val_accuracy: 0.8462 - lr: 5.0000e-06\n",
            "Epoch 15/100\n",
            "78/85 [==========================>...] - ETA: 0s - loss: 0.3822 - accuracy: 0.8257\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-07.\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3784 - accuracy: 0.8286 - val_loss: 0.3494 - val_accuracy: 0.8462 - lr: 5.0000e-06\n",
            "Epoch 16/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.3755 - accuracy: 0.8368 - val_loss: 0.3493 - val_accuracy: 0.8462 - lr: 5.0000e-07\n",
            "Epoch 17/100\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3745 - accuracy: 0.8372 - val_loss: 0.3493 - val_accuracy: 0.8462 - lr: 5.0000e-07\n",
            "Epoch 18/100\n",
            "76/85 [=========================>....] - ETA: 0s - loss: 0.3748 - accuracy: 0.8339\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 4.999999987376214e-08.\n",
            "85/85 [==============================] - 1s 7ms/step - loss: 0.3793 - accuracy: 0.8309 - val_loss: 0.3492 - val_accuracy: 0.8439 - lr: 5.0000e-07\n",
            "Epoch 19/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.3893 - accuracy: 0.8264 - val_loss: 0.3492 - val_accuracy: 0.8439 - lr: 5.0000e-08\n",
            "Epoch 20/100\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.3777 - accuracy: 0.8338 - val_loss: 0.3492 - val_accuracy: 0.8439 - lr: 5.0000e-08\n",
            "Epoch 21/100\n",
            "82/85 [===========================>..] - ETA: 0s - loss: 0.3829 - accuracy: 0.8239Restoring model weights from the end of the best epoch: 12.\n",
            "\n",
            "Epoch 21: ReduceLROnPlateau reducing learning rate to 5.000000058430488e-09.\n",
            "85/85 [==============================] - 1s 8ms/step - loss: 0.3820 - accuracy: 0.8253 - val_loss: 0.3492 - val_accuracy: 0.8439 - lr: 5.0000e-08\n",
            "Epoch 21: early stopping\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Define the callbacks\n",
        "early_stopping = EarlyStopping(monitor='val_accuracy', patience=9, verbose=1, restore_best_weights=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_accuracy', factor=0.1, patience=3, verbose=1)\n",
        "model_checkpoint = ModelCheckpoint('best_weights.h5', monitor='val_accuracy', save_best_only=True)\n",
        "\n",
        "input_shape1 = (1024,)\n",
        "input_shape2 = (1024,)\n",
        "\n",
        "# Define the inputs for the two branches of the network\n",
        "input1 = Input(shape=input_shape1)\n",
        "input2 = Input(shape=input_shape2)\n",
        "\n",
        "# Define the layers for the first branch of the network\n",
        "x1 = Dense(512, activation='relu')(input1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(256, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(128, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(64, activation='relu')(x1)\n",
        "output1 = x1\n",
        "modeldrug = Model(input1, output1)\n",
        "modeldrug.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Define the layers for the second branch of the network\n",
        "x2 = Dense(512, activation='relu')(input2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(256, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(128, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(64, activation='relu')(x2)\n",
        "output2 = x2\n",
        "modeldisease = Model(input2, output2)\n",
        "modeldisease.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Subtract the embeddings from the two branches\n",
        "diff = Subtract()([output1, output2])\n",
        "diff = Lambda(lambda d: abs(d))(diff)\n",
        "# diff = Dropout(0.2)(diff)\n",
        "concat = Dense(32, activation= 'relu')(diff)\n",
        "# concat = Dropout(rate=0.2)(concat)\n",
        "\n",
        "\n",
        "# Define the output layer\n",
        "output = Dense(1, activation='sigmoid')(concat)\n",
        "\n",
        "# Define the model\n",
        "simmodel = Model(inputs=[input1, input2], outputs=output)\n",
        "\n",
        "# Define the optimizer and compile the model\n",
        "optimizer = Adam(learning_rate=0.00005)\n",
        "simmodel.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "\n",
        "# # Define the class weights\n",
        "class_weights = {0: 1, 1: 1966/748 } # inverse frequency weighting\n",
        "\n",
        "\n",
        "# Train the model on your data with the defined callbacks\n",
        "\n",
        "X_me = np.array(X_me)\n",
        "Y_me = np.array(Y_me)\n",
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "l_v = np.array(X_me[:, :1024])\n",
        "r_v = np.array(X_me[:, 1024:])\n",
        "\n",
        "x_train, x_val, y_train, y_val = train_test_split(X, y)\n",
        "\n",
        "l = np.array(x_train[:, :1024])\n",
        "r = np.array(x_train[:, 1024:])\n",
        "\n",
        "\n",
        "l_val = np.array(x_val[:, :1024])\n",
        "r_val = np.array(x_val[:, 1024:])\n",
        "\n",
        "history = simmodel.fit([l, r], y_train, batch_size=32, epochs=100,validation_data=([l_val, r_val], y_val), callbacks=[early_stopping, reduce_lr, model_checkpoint])\n",
        "\n",
        "# history = simmodel.fit([l, r], y_train, batch_size= 32, epochs= 300, class_weight=class_weights, validation_data=([l_val, r_val],y_val)\n",
        "                  # ,callbacks=[callback]\n",
        "                  # )\n",
        "model_history(history)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "tJyaKHY2lGHK",
        "outputId": "6c6d7f68-3dab-4186-f5d3-de6e9e5040c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5/5 [==============================] - 0s 5ms/step - loss: 0.3236 - accuracy: 0.8684\n",
            "[0.3236018121242523, 0.8684210777282715]\n",
            "5/5 [==============================] - 0s 3ms/step\n",
            "auc-roc =  0.9372834372834373\n",
            "auc-pr =  0.9249771050165928\n",
            "Brier score: 0.09754309566486338\n",
            "Cohen Kapp score: 0.7370242214532872\n",
            "MCC score: 0.738045738045738\n"
          ]
        }
      ],
      "source": [
        "print(simmodel.evaluate([l_v, r_v], Y_me))\n",
        "y_hat= simmodel.predict([l_v, r_v])\n",
        "A = roc_auc_score(Y_me, y_hat)\n",
        "print(\"auc-roc = \", A)\n",
        "\n",
        "precision, recall, thresholds = precision_recall_curve(Y_me, y_hat)\n",
        "PR = auc(recall, precision)\n",
        "\n",
        "print(\"auc-pr = \", PR)\n",
        "\n",
        "print(\"Brier score:\", brier_score_loss(Y_me, y_hat))\n",
        "\n",
        "y_hat_e = []\n",
        "for item in y_hat:\n",
        "  if item >= 0.5:\n",
        "    y_hat_e.append(1)\n",
        "  else:\n",
        "    y_hat_e.append(0)\n",
        "print(\"Cohen Kapp score:\", cohen_kappa_score(Y_me, y_hat_e))\n",
        "print(\"MCC score:\", matthews_corrcoef(Y_me, y_hat_e))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "D1GhuQZ-I5-F"
      },
      "source": [
        "## First oversample, Next Undersample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Xd3osRxUL4Z9",
        "outputId": "b29d7734-b862-4ab3-c75f-31616ef77008"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3587\n",
            "3398\n"
          ]
        }
      ],
      "source": [
        "\n",
        "frames = [has, hasnt]\n",
        "\n",
        "f = pd.concat(frames)\n",
        "X=[]\n",
        "for idx, row in f.iterrows():\n",
        "  X.append(np.concatenate((model1.wv[(row.drug_id).lower()].reshape((1,1024)), model1.wv[(row.ind_id).lower()].reshape((1,1024))), axis=None))\n",
        "\n",
        "y=np.zeros((len(X), ))\n",
        "\n",
        "for i in range(has.shape[0]):\n",
        "  y[i]=1\n",
        "\n",
        "from imblearn.over_sampling import SMOTE, RandomOverSampler\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "over = SMOTE(sampling_strategy=0.9)\n",
        "# over = RandomOverSampler(0.9)\n",
        "under = RandomUnderSampler()\n",
        "X, y = over.fit_resample(X,y)\n",
        "print(len(X))\n",
        "X,y = under.fit_resample(X,y)\n",
        "print(len(X))\n",
        "\n",
        "\n",
        "# the main test set\n",
        "X_me = []\n",
        "Y_me = []\n",
        "for a in A_test:\n",
        "  sample = a.split(\" \")\n",
        "  dname =sample[0]\n",
        "  disname = sample[2].split(\".\")\n",
        "  X_me.append(np.concatenate((model1.wv[(dname.rstrip().lower())].reshape((1, 1024)), model1.wv[(disname[0].lstrip().lower())].reshape((1, 1024))), axis=None))\n",
        "  Y_me.append(1)\n",
        "for idx, i in I.iterrows():\n",
        "  if(i.ind_id in my_disease_list and i.drug_id in my_drug_list):\n",
        "    X_me.append(np.concatenate((model1.wv[i.drug_id.lower()].reshape((1, 1024)), model1.wv[i.ind_id.lower()].reshape((1, 1024))), axis=None))\n",
        "    Y_me.append(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "IulJbCQm9jUS",
        "outputId": "b7228fcb-f8d5-4d45-9886-c0bb3e8a8e5e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "80/80 [==============================] - 6s 11ms/step - loss: 0.6833 - accuracy: 0.5581 - val_loss: 0.6583 - val_accuracy: 0.7141 - lr: 5.0000e-05\n",
            "Epoch 2/100\n",
            "80/80 [==============================] - 1s 8ms/step - loss: 0.6200 - accuracy: 0.7567 - val_loss: 0.5539 - val_accuracy: 0.8035 - lr: 5.0000e-05\n",
            "Epoch 3/100\n",
            "80/80 [==============================] - 1s 8ms/step - loss: 0.5086 - accuracy: 0.8077 - val_loss: 0.4407 - val_accuracy: 0.8212 - lr: 5.0000e-05\n",
            "Epoch 4/100\n",
            "80/80 [==============================] - 1s 7ms/step - loss: 0.4327 - accuracy: 0.8171 - val_loss: 0.3961 - val_accuracy: 0.8212 - lr: 5.0000e-05\n",
            "Epoch 5/100\n",
            "80/80 [==============================] - 1s 8ms/step - loss: 0.4058 - accuracy: 0.8277 - val_loss: 0.3766 - val_accuracy: 0.8400 - lr: 5.0000e-05\n",
            "Epoch 6/100\n",
            "80/80 [==============================] - 1s 9ms/step - loss: 0.3828 - accuracy: 0.8312 - val_loss: 0.3700 - val_accuracy: 0.8447 - lr: 5.0000e-05\n",
            "Epoch 7/100\n",
            "80/80 [==============================] - 1s 7ms/step - loss: 0.3794 - accuracy: 0.8320 - val_loss: 0.3614 - val_accuracy: 0.8353 - lr: 5.0000e-05\n",
            "Epoch 8/100\n",
            "80/80 [==============================] - 1s 7ms/step - loss: 0.3631 - accuracy: 0.8509 - val_loss: 0.3547 - val_accuracy: 0.8376 - lr: 5.0000e-05\n",
            "Epoch 9/100\n",
            "80/80 [==============================] - 1s 8ms/step - loss: 0.3648 - accuracy: 0.8454 - val_loss: 0.3548 - val_accuracy: 0.8459 - lr: 5.0000e-05\n",
            "Epoch 10/100\n",
            "80/80 [==============================] - 1s 9ms/step - loss: 0.3579 - accuracy: 0.8422 - val_loss: 0.3535 - val_accuracy: 0.8518 - lr: 5.0000e-05\n",
            "Epoch 11/100\n",
            "80/80 [==============================] - 1s 8ms/step - loss: 0.3570 - accuracy: 0.8430 - val_loss: 0.3508 - val_accuracy: 0.8482 - lr: 5.0000e-05\n",
            "Epoch 12/100\n",
            "80/80 [==============================] - 1s 8ms/step - loss: 0.3510 - accuracy: 0.8493 - val_loss: 0.3455 - val_accuracy: 0.8471 - lr: 5.0000e-05\n",
            "Epoch 13/100\n",
            "71/80 [=========================>....] - ETA: 0s - loss: 0.3462 - accuracy: 0.8530\n",
            "Epoch 13: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-06.\n",
            "80/80 [==============================] - 1s 7ms/step - loss: 0.3467 - accuracy: 0.8520 - val_loss: 0.3553 - val_accuracy: 0.8424 - lr: 5.0000e-05\n",
            "Epoch 14/100\n",
            "80/80 [==============================] - 1s 7ms/step - loss: 0.3491 - accuracy: 0.8513 - val_loss: 0.3458 - val_accuracy: 0.8482 - lr: 5.0000e-06\n",
            "Epoch 15/100\n",
            "80/80 [==============================] - 1s 10ms/step - loss: 0.3385 - accuracy: 0.8556 - val_loss: 0.3479 - val_accuracy: 0.8494 - lr: 5.0000e-06\n",
            "Epoch 16/100\n",
            "79/80 [============================>.] - ETA: 0s - loss: 0.3456 - accuracy: 0.8528\n",
            "Epoch 16: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-07.\n",
            "80/80 [==============================] - 1s 10ms/step - loss: 0.3459 - accuracy: 0.8524 - val_loss: 0.3460 - val_accuracy: 0.8471 - lr: 5.0000e-06\n",
            "Epoch 17/100\n",
            "80/80 [==============================] - 1s 10ms/step - loss: 0.3410 - accuracy: 0.8552 - val_loss: 0.3459 - val_accuracy: 0.8471 - lr: 5.0000e-07\n",
            "Epoch 18/100\n",
            "80/80 [==============================] - 1s 10ms/step - loss: 0.3378 - accuracy: 0.8532 - val_loss: 0.3461 - val_accuracy: 0.8482 - lr: 5.0000e-07\n",
            "Epoch 19/100\n",
            "75/80 [===========================>..] - ETA: 0s - loss: 0.3450 - accuracy: 0.8558Restoring model weights from the end of the best epoch: 10.\n",
            "\n",
            "Epoch 19: ReduceLROnPlateau reducing learning rate to 4.999999987376214e-08.\n",
            "80/80 [==============================] - 1s 7ms/step - loss: 0.3450 - accuracy: 0.8564 - val_loss: 0.3459 - val_accuracy: 0.8471 - lr: 5.0000e-07\n",
            "Epoch 19: early stopping\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Define the callbacks\n",
        "early_stopping = EarlyStopping(monitor='val_accuracy', patience=9, verbose=1, restore_best_weights=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_accuracy', factor=0.1, patience=3, verbose=1)\n",
        "model_checkpoint = ModelCheckpoint('best_weights.h5', monitor='val_accuracy', save_best_only=True)\n",
        "\n",
        "input_shape1 = (1024,)\n",
        "input_shape2 = (1024,)\n",
        "\n",
        "# Define the inputs for the two branches of the network\n",
        "input1 = Input(shape=input_shape1)\n",
        "input2 = Input(shape=input_shape2)\n",
        "\n",
        "# Define the layers for the first branch of the network\n",
        "x1 = Dense(512, activation='relu')(input1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(256, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(128, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(64, activation='relu')(x1)\n",
        "output1 = x1\n",
        "modeldrug = Model(input1, output1)\n",
        "modeldrug.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Define the layers for the second branch of the network\n",
        "x2 = Dense(512, activation='relu')(input2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(256, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(128, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(64, activation='relu')(x2)\n",
        "output2 = x2\n",
        "modeldisease = Model(input2, output2)\n",
        "modeldisease.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Subtract the embeddings from the two branches\n",
        "diff = Subtract()([output1, output2])\n",
        "diff = Lambda(lambda d: abs(d))(diff)\n",
        "# diff = Dropout(0.2)(diff)\n",
        "# Concatenate the absolute difference with the embeddings\n",
        "# concat = concatenate([output1, output2])\n",
        "concat = Dense(32, activation= 'relu')(diff)\n",
        "# concat = Dropout(rate=0.2)(concat)\n",
        "\n",
        "\n",
        "# Define the output layer\n",
        "output = Dense(1, activation='sigmoid')(concat)\n",
        "\n",
        "# Define the model\n",
        "simmodel = Model(inputs=[input1, input2], outputs=output)\n",
        "\n",
        "# Define the optimizer and compile the model\n",
        "optimizer = Adam(learning_rate=0.00005)\n",
        "simmodel.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "\n",
        "# # Define the class weights\n",
        "class_weights = {0: 1, 1: 1966/748 } # inverse frequency weighting\n",
        "\n",
        "\n",
        "# Train the model on your data with the defined callbacks\n",
        "\n",
        "X_me = np.array(X_me)\n",
        "Y_me = np.array(Y_me)\n",
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "l_v = np.array(X_me[:, :1024])\n",
        "r_v = np.array(X_me[:, 1024:])\n",
        "\n",
        "x_train, x_val, y_train, y_val = train_test_split(X, y)\n",
        "\n",
        "l = np.array(x_train[:, :1024])\n",
        "r = np.array(x_train[:, 1024:])\n",
        "\n",
        "\n",
        "l_val = np.array(x_val[:, :1024])\n",
        "r_val = np.array(x_val[:, 1024:])\n",
        "\n",
        "history = simmodel.fit([l, r], y_train, batch_size=32, epochs=100,validation_data=([l_val, r_val], y_val), callbacks=[early_stopping, reduce_lr, model_checkpoint])\n",
        "\n",
        "# history = simmodel.fit([l, r], y_train, batch_size= 32, epochs= 300, class_weight=class_weights, validation_data=([l_val, r_val],y_val)\n",
        "                  # ,callbacks=[callback]\n",
        "                  # )\n",
        "model_history(history)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3NH-0Xt4iixd"
      },
      "outputs": [],
      "source": [
        "print(simmodel.evaluate([l_v, r_v], Y_me))\n",
        "y_hat= simmodel.predict([l_v, r_v])\n",
        "A = roc_auc_score(Y_me, y_hat)\n",
        "print(\"auc-roc = \", A)\n",
        "\n",
        "precision, recall, thresholds = precision_recall_curve(Y_me, y_hat)\n",
        "PR = auc(recall, precision)\n",
        "\n",
        "print(\"auc-pr = \", PR)\n",
        "\n",
        "print(\"Brier score:\", brier_score_loss(Y_me, y_hat))\n",
        "\n",
        "y_hat_e = []\n",
        "for item in y_hat:\n",
        "  if item >= 0.5:\n",
        "    y_hat_e.append(1)\n",
        "  else:\n",
        "    y_hat_e.append(0)\n",
        "print(\"Cohen Kapp score:\", cohen_kappa_score(Y_me, y_hat_e))\n",
        "print(\"MCC score:\", matthews_corrcoef(Y_me, y_hat_e))\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "sCs2OJFUpPsP"
      },
      "source": [
        "# Covid Drugs"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "aVuY5FiCvt_A"
      },
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "hgS3UGCwpOcD",
        "outputId": "4524e8c0-f6ea-41f5-afe5-76a9d3ee5006"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-97671e10-da1c-4e81-9fc6-d82d2d76edbe\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Drug name</th>\n",
              "      <th>drug_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ritonavir</td>\n",
              "      <td>DB00503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chloroquine</td>\n",
              "      <td>DB00608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Darunavir</td>\n",
              "      <td>DB01264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lopinavir</td>\n",
              "      <td>DB01601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Galidesivir</td>\n",
              "      <td>DB11676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Fostamatinib</td>\n",
              "      <td>DB12010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Sotrovimab</td>\n",
              "      <td>DB16355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>MM3122</td>\n",
              "      <td>DB16737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Cilgavimab</td>\n",
              "      <td>DB16393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Tixagevimab</td>\n",
              "      <td>DB16394</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>68 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97671e10-da1c-4e81-9fc6-d82d2d76edbe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-97671e10-da1c-4e81-9fc6-d82d2d76edbe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-97671e10-da1c-4e81-9fc6-d82d2d76edbe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       Drug name  drug_id\n",
              "0      Ritonavir  DB00503\n",
              "1    Chloroquine  DB00608\n",
              "2      Darunavir  DB01264\n",
              "3      Lopinavir  DB01601\n",
              "4    Galidesivir  DB11676\n",
              "..           ...      ...\n",
              "63  Fostamatinib  DB12010\n",
              "64    Sotrovimab  DB16355\n",
              "65        MM3122  DB16737\n",
              "66    Cilgavimab  DB16393\n",
              "67   Tixagevimab  DB16394\n",
              "\n",
              "[68 rows x 2 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ass = pd.read_csv(\"my_repodb.txt\")\n",
        "ass.set_index(\"Unnamed: 0\", inplace=True)\n",
        "disgenet = pd.read_csv(\"Gen-Dis-association-Disgenet-2022 - curated_gene_disease_associations CORONA VIRUS INFECTION.csv\")\n",
        "\n",
        "# making the drug and disease lists\n",
        "my_disease_list = list(set(ass.ind_id))\n",
        "my_disease_list.append(\"C0206750\")\n",
        "my_drug_list = list(set(ass.drug_id))\n",
        "drugs=list(set(ass.drug_id))\n",
        "disgenet = disgenet.loc[disgenet['diseaseId'].isin(my_disease_list)]\n",
        "my_disease_list = list(set(disgenet.diseaseId))\n",
        "ass = ass.loc[ass['ind_id'].isin(my_disease_list)]\n",
        "my_drug_list = list(set(ass.drug_id))\n",
        "\n",
        "\n",
        "covid_drugs= pd.read_csv(\"Covid-19 drugs from drugbank.csv\")\n",
        "covid_drugs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "hGMjEupafIN6"
      },
      "outputs": [],
      "source": [
        "# Saxagliptin DB06335\n",
        "# Vildagliptin DB04876\n",
        "# Linagliptin DB08882\n",
        "# Sitagliptin DB01261\n",
        "# Alogliptin DB06203\n",
        "\n",
        "gliptins=['DB06335', 'DB04876','DB08882','DB01261','DB06203' ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2P47MfmEqo44",
        "outputId": "483d022b-8127-4955-f1a6-a81ca6ddf771"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "7\n"
          ]
        }
      ],
      "source": [
        "i=0\n",
        "my_covid_drug = []\n",
        "for d in covid_drugs.drug_id:\n",
        "  if d in my_drug_list:\n",
        "    my_covid_drug.append(d)\n",
        "    i+=1\n",
        "print(i)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "xm8q7VkYrf7S"
      },
      "outputs": [],
      "source": [
        "has = pd.read_csv(\"my_repodb.txt\") # Positive data: Known Drug-Disease associations\n",
        "has.set_index(\"Unnamed: 0\", inplace=True)\n",
        "has = has.drop(['drug_name', 'ind_name','NCT','status', 'phase',\n",
        "       'DetailedStatus'], axis=1)\n",
        "has = has.loc[has['ind_id'].isin(my_disease_list)]\n",
        "\n",
        "\n",
        "hasnt = pd.read_csv(\"my_only_negatives.txt\") # Negative data: Unknown Drug-Disease associations when disease is adverse drug reaction\n",
        "hasnt.set_index(\"Unnamed: 0\", inplace=True)\n",
        "hasnt = hasnt.rename(columns={\"Not_indicated_for\":\"ind_id\", \"Drug_id\": \"drug_id\"})\n",
        "hasnt = hasnt.loc[hasnt['ind_id'].isin(my_disease_list)]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "4HZHiP7Qr1GQ"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "# making the relation of diseases: has_geneid, has_class, has_semantic, has_type\n",
        "\n",
        "C=[]\n",
        "num1=0\n",
        "num2=0\n",
        "num3=0\n",
        "num4=0\n",
        "for idx, row in disgenet.iterrows():\n",
        "  # if row.diseaseId in my_disease_list:\n",
        "      semtype = \"_\".join(row.diseaseSemanticType.split())\n",
        "      clas = row.diseaseClass.split(\";\")\n",
        "      text1 =str(row.diseaseId)+\" has_geneid \"+str(row.geneSymbol)+\". \"\n",
        "      num1+=1\n",
        "      text2 =str(row.diseaseId)+\" has_type \"+str(row.diseaseType)+\". \"\n",
        "      num2+=1\n",
        "      text3 =str(row.diseaseId)+\" has_semantic \"+str(semtype)+\". \"\n",
        "      num3+=1\n",
        "      C.append(text1)\n",
        "      C.append(text2)\n",
        "      C.append(text3)\n",
        "      num4+=len(clas)\n",
        "      for cl in clas:\n",
        "        text =str(row.diseaseId)+\" has_class \"+str(cl)+\". \"\n",
        "        C.append(text)\n",
        "\n",
        "Disease=\"\"\n",
        "for c in C:\n",
        "  Disease= Disease+c\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "cWlM-xeNr1GS"
      },
      "source": [
        "making the drug relations: has_sideeffect, has_target, has_domain, has_atccode, has_chemicalstructure"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "LZ_2hzphr1GT"
      },
      "outputs": [],
      "source": [
        "# has_target relation. data is collected using DrugBank\n",
        "T=[]\n",
        "target= pd.read_csv(\"my_target.txt\")\n",
        "target.set_index(\"Unnamed: 0\", inplace=True)\n",
        "for idx, row in target.iterrows():\n",
        "  for c in target.columns:\n",
        "    if row[c] ==1 :\n",
        "      text =str(idx)+\" has_target \"+str(c)+\". \"\n",
        "      T.append(text)\n",
        "target1=\"\"\n",
        "for c in T:\n",
        "  target1= target1+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "zQ5LceZhr1GU"
      },
      "outputs": [],
      "source": [
        "# has_domain relation. data is collected using UniProt\n",
        "D=[]\n",
        "dom= pd.read_csv(\"my_domain.txt\")\n",
        "dom.set_index(\"Unnamed: 0\", inplace=True)\n",
        "for idx, row in dom.iterrows():\n",
        "  for c in dom.columns:\n",
        "    if row[c] ==1 :\n",
        "      text =str(idx)+\" has_domain \"+str(c)+\". \"\n",
        "      D.append(text)\n",
        "\n",
        "domain=\"\"\n",
        "for c in D:\n",
        "  domain= domain+c\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "gzSMNVGmr1GU"
      },
      "outputs": [],
      "source": [
        "# has_chemical relation. data is collected using Pubchem\n",
        "C=[]\n",
        "finger= pd.read_csv(\"my_finger.txt\")\n",
        "finger.set_index(\"Unnamed: 0\", inplace=True)\n",
        "for idx, row in finger.iterrows():\n",
        "  for c in finger.columns:\n",
        "    if row[c] ==1 :\n",
        "      text =str(idx)+\" has_chemical S\"+str(c)+\". \"\n",
        "      C.append(text)\n",
        "chemical=\"\"\n",
        "for c in C:\n",
        "  chemical= chemical+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "2wjHaae0r1GV"
      },
      "outputs": [],
      "source": [
        "# has_atc_code relation. data is collected using Sider4.1\n",
        "\n",
        "ATC=[]\n",
        "atc= pd.read_csv(\"my_ATC.txt\")\n",
        "atc.set_index(\"Unnamed: 0\", inplace=True)\n",
        "for idx, row in atc.iterrows():\n",
        "  for c in atc.columns:\n",
        "    if row[c] == 1 and (len(c)==4 or len(c)==1 or len(c)==3 or len(c)==5): # we consider first 4 level of ATC code\n",
        "      text =str(idx)+\" has_atc_code \"+str(c)+\". \"\n",
        "      ATC.append(text)\n",
        "atccode=\"\"\n",
        "for c in ATC:\n",
        "  atccode= atccode+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "6cFK6ZCdr1GV"
      },
      "outputs": [],
      "source": [
        "# has_sideeffect relation. Date is collected using Sider4.1\n",
        "S=[]\n",
        "side= pd.read_csv(\"my_sideeffect.txt\")\n",
        "side.set_index(\"Unnamed: 0\", inplace=True)\n",
        "for idx, row in side.iterrows():\n",
        "  for c in side.columns:\n",
        "    if row[c] ==1 :\n",
        "      text =str(idx)+\" has_sideeffect \"+str(c)+\". \"\n",
        "      S.append(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "NZ9m2p_KF6aA"
      },
      "outputs": [],
      "source": [
        "PPI = pd.read_csv(\"PPI-extracted scores more than 80-cleaned.txt\")\n",
        "p=[]\n",
        "for idx, row in PPI.iterrows():\n",
        "  if row.P1 in target.columns and row.P2 in target.columns:\n",
        "    text = str(row.P1)+' has_ppi_interaction '+ str(row.P2)+'. '\n",
        "    p.append(text)\n",
        "ppi=\"\"\n",
        "for c in p:\n",
        "  ppi= ppi+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "8aFIt57VF6aB"
      },
      "outputs": [],
      "source": [
        "gensymbol = set(list(disgenet.geneSymbol))\n",
        "GRN = pd.read_csv(\"GRN-extracted scores more than 90-cleaned.txt\")\n",
        "G=[]\n",
        "for idx, row in GRN.iterrows():\n",
        "  if row.G1 in gensymbol and row.G2 in gensymbol:\n",
        "    text = str(row.G1)+' has_GRN_interaction '+ str(row.G2)+'. '\n",
        "    G.append(text)\n",
        "grn=\"\"\n",
        "for c in G:\n",
        "  grn= grn+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "hROCOLqxGCwc"
      },
      "outputs": [],
      "source": [
        "# target-domain relation. Date is collected using target_domain_association\n",
        "TD=[]\n",
        "td= pd.read_csv(\"target_domain_association.txt\")\n",
        "td\n",
        "td.set_index(\"Unnamed: 0\", inplace=True)\n",
        "td\n",
        "for idx, row in td.iterrows():\n",
        "  if idx in target.columns:\n",
        "    for c in td.columns:\n",
        "      if row[c] ==1 and c in dom.columns:\n",
        "        text =str(idx)+\" has_tardom \"+str(c)+\". \"\n",
        "        TD.append(text)\n",
        "\n",
        "tardom=\"\"\n",
        "for c in TD:\n",
        "  tardom= tardom+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Dwu4OpsGGCwc"
      },
      "outputs": [],
      "source": [
        "# target-gene relation. Date is collected using DisGenet\n",
        "genid={}\n",
        "for idx, row in disgenet.iterrows():\n",
        "  # print(row)\n",
        "  if row.geneId in genid.keys():\n",
        "    continue\n",
        "  else:\n",
        "    genid[row.geneId]=row.geneSymbol\n",
        "TG=[]\n",
        "tg= pd.read_csv(\"Uniprot-2-GeneID.csv\")\n",
        "tg\n",
        "\n",
        "for idx, row in tg.iterrows():\n",
        "  if row.UniProtKB in target.columns:\n",
        "    if row.GENEID in genid.keys():\n",
        "      text =str(row.UniProtKB)+\" has_targen \"+str(row.GENEID)+\". \"\n",
        "      TG.append(text)\n",
        "\n",
        "targene=\"\"\n",
        "for c in TG:\n",
        "  targene= targene+c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "kMGpynGCsGQP"
      },
      "outputs": [],
      "source": [
        "A=[]\n",
        "for ix, row in ass.iterrows():\n",
        "  text = str(row.drug_id)+\" has_indicated \"+str(row.ind_id)+\". \"\n",
        "  A.append(text)\n",
        "\n",
        "ass=\"\" # making the new associations for training the models\n",
        "for c in A:\n",
        "  ass= ass+c\n",
        "\n",
        "side=\"\"\n",
        "for c in S:\n",
        "  side= side+c\n",
        "\n",
        "I = hasnt"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "60eP_PiC7Dze"
      },
      "source": [
        "## Word2Vec"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "M0aV8hRG689i"
      },
      "outputs": [],
      "source": [
        "\n",
        "data=[]\n",
        "sets=[chemical, target1, side, domain, atccode,ppi, grn, tardom, targene, ass, Disease]\n",
        "\n",
        "for s in sets:\n",
        "  for i in sent_tokenize(s):\n",
        "      temp = []\n",
        "      # tokenize the sentence into words\n",
        "      for j in word_tokenize(i):\n",
        "          temp.append(j.lower())\n",
        "      data.append(temp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LLug2LnG689k",
        "outputId": "c08f8c73-0297-43ed-f4a3-8bcf4fd85029"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "12825"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "word_freq = defaultdict(int)\n",
        "for sent in data:\n",
        "    for i in sent:\n",
        "        word_freq[i] += 1\n",
        "len(word_freq)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ufx5RF_F689m",
        "outputId": "10b09418-290c-4b32-b6d5-3140448c816c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:gensim.models.keyedvectors:destructive init_sims(replace=True) deprecated & no longer required for space-efficiency\n"
          ]
        }
      ],
      "source": [
        "model1 = gensim.models.Word2Vec(min_count = 1, alpha=0.001, vector_size = 1024, window = 2, epochs=100, sg=0)\n",
        "model1.build_vocab(data, progress_per=10000)\n",
        "model1.train(data, total_examples=model1.corpus_count, epochs=500, report_delay=1)\n",
        "model1.init_sims(replace=True)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "UMbbKYXEx8jG"
      },
      "source": [
        "## Test on COVID-19"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "sN3a1-Nwx_v6"
      },
      "outputs": [],
      "source": [
        "frames = [has, hasnt]\n",
        "\n",
        "f = pd.concat(frames)\n",
        "X=[]\n",
        "for idx, row in f.iterrows():\n",
        "  X.append(np.concatenate((model1.wv[(row.drug_id).lower()], model1.wv[(row.ind_id).lower()]), axis=None))\n",
        "y=np.zeros((len(X), ))\n",
        "\n",
        "for i in range(has.shape[0]):\n",
        "  y[i]=1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikyw8bqRx_v7",
        "outputId": "83b28f1f-c97d-49b7-a970-20df7fc79c69"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3735\n",
            "3538\n"
          ]
        }
      ],
      "source": [
        "from imblearn.over_sampling import SMOTE, RandomOverSampler\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "over = SMOTE(sampling_strategy=0.9)\n",
        "# over = RandomOverSampler(0.9)\n",
        "under = RandomUnderSampler()\n",
        "X, y = over.fit_resample(X,y)\n",
        "print(len(X))\n",
        "X,y = under.fit_resample(X,y)\n",
        "print(len(X))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_0J4KEulx_v8",
        "outputId": "0a780092-4c84-4484-dd4c-aa02cc7f982a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "83/83 [==============================] - 4s 13ms/step - loss: 0.6741 - accuracy: 0.7158 - val_loss: 0.6281 - val_accuracy: 0.7887 - lr: 5.0000e-05\n",
            "Epoch 2/100\n",
            "83/83 [==============================] - 1s 10ms/step - loss: 0.5633 - accuracy: 0.7976 - val_loss: 0.4841 - val_accuracy: 0.8158 - lr: 5.0000e-05\n",
            "Epoch 3/100\n",
            "83/83 [==============================] - 1s 10ms/step - loss: 0.4408 - accuracy: 0.8198 - val_loss: 0.4341 - val_accuracy: 0.8249 - lr: 5.0000e-05\n",
            "Epoch 4/100\n",
            "83/83 [==============================] - 1s 9ms/step - loss: 0.4015 - accuracy: 0.8277 - val_loss: 0.4161 - val_accuracy: 0.8249 - lr: 5.0000e-05\n",
            "Epoch 5/100\n",
            "83/83 [==============================] - 1s 10ms/step - loss: 0.3916 - accuracy: 0.8292 - val_loss: 0.4111 - val_accuracy: 0.8260 - lr: 5.0000e-05\n",
            "Epoch 6/100\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 0.3787 - accuracy: 0.8391 - val_loss: 0.4033 - val_accuracy: 0.8203 - lr: 5.0000e-05\n",
            "Epoch 7/100\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 0.3810 - accuracy: 0.8387 - val_loss: 0.4014 - val_accuracy: 0.8249 - lr: 5.0000e-05\n",
            "Epoch 8/100\n",
            "83/83 [==============================] - 1s 7ms/step - loss: 0.3726 - accuracy: 0.8458 - val_loss: 0.3923 - val_accuracy: 0.8305 - lr: 5.0000e-05\n",
            "Epoch 9/100\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 0.3573 - accuracy: 0.8507 - val_loss: 0.4003 - val_accuracy: 0.8203 - lr: 5.0000e-05\n",
            "Epoch 10/100\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 0.3621 - accuracy: 0.8462 - val_loss: 0.3887 - val_accuracy: 0.8215 - lr: 5.0000e-05\n",
            "Epoch 11/100\n",
            "82/83 [============================>.] - ETA: 0s - loss: 0.3600 - accuracy: 0.8468\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-06.\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 0.3617 - accuracy: 0.8466 - val_loss: 0.3870 - val_accuracy: 0.8294 - lr: 5.0000e-05\n",
            "Epoch 12/100\n",
            "83/83 [==============================] - 1s 7ms/step - loss: 0.3527 - accuracy: 0.8458 - val_loss: 0.3836 - val_accuracy: 0.8384 - lr: 5.0000e-06\n",
            "Epoch 13/100\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 0.3558 - accuracy: 0.8440 - val_loss: 0.3833 - val_accuracy: 0.8373 - lr: 5.0000e-06\n",
            "Epoch 14/100\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 0.3479 - accuracy: 0.8560 - val_loss: 0.3826 - val_accuracy: 0.8384 - lr: 5.0000e-06\n",
            "Epoch 15/100\n",
            "73/83 [=========================>....] - ETA: 0s - loss: 0.3570 - accuracy: 0.8485\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-07.\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 0.3516 - accuracy: 0.8522 - val_loss: 0.3827 - val_accuracy: 0.8362 - lr: 5.0000e-06\n",
            "Epoch 16/100\n",
            "83/83 [==============================] - 0s 6ms/step - loss: 0.3463 - accuracy: 0.8560 - val_loss: 0.3826 - val_accuracy: 0.8373 - lr: 5.0000e-07\n",
            "Epoch 17/100\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 0.3533 - accuracy: 0.8530 - val_loss: 0.3825 - val_accuracy: 0.8373 - lr: 5.0000e-07\n",
            "Epoch 18/100\n",
            "83/83 [==============================] - ETA: 0s - loss: 0.3500 - accuracy: 0.8477\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 4.999999987376214e-08.\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 0.3500 - accuracy: 0.8477 - val_loss: 0.3824 - val_accuracy: 0.8373 - lr: 5.0000e-07\n",
            "Epoch 19/100\n",
            "83/83 [==============================] - 0s 6ms/step - loss: 0.3497 - accuracy: 0.8500 - val_loss: 0.3824 - val_accuracy: 0.8384 - lr: 5.0000e-08\n",
            "Epoch 20/100\n",
            "83/83 [==============================] - 1s 7ms/step - loss: 0.3573 - accuracy: 0.8530 - val_loss: 0.3824 - val_accuracy: 0.8384 - lr: 5.0000e-08\n",
            "Epoch 21/100\n",
            "76/83 [==========================>...] - ETA: 0s - loss: 0.3515 - accuracy: 0.8528Restoring model weights from the end of the best epoch: 12.\n",
            "\n",
            "Epoch 21: ReduceLROnPlateau reducing learning rate to 5.000000058430488e-09.\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 0.3535 - accuracy: 0.8507 - val_loss: 0.3824 - val_accuracy: 0.8384 - lr: 5.0000e-08\n",
            "Epoch 21: early stopping\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "\n",
        "# Define the callbacks\n",
        "early_stopping = EarlyStopping(monitor='val_accuracy', patience=9, verbose=1, restore_best_weights=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_accuracy', factor=0.1, patience=3, verbose=1)\n",
        "model_checkpoint = ModelCheckpoint('best_weights.h5', monitor='val_accuracy', save_best_only=True)\n",
        "\n",
        "input_shape1 = (1024,)\n",
        "input_shape2 = (1024,)\n",
        "\n",
        "# Define the inputs for the two branches of the network\n",
        "input1 = Input(shape=input_shape1)\n",
        "input2 = Input(shape=input_shape2)\n",
        "\n",
        "# Define the layers for the first branch of the network\n",
        "x1 = Dense(512, activation='relu')(input1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(256, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(128, activation='relu')(x1)\n",
        "x1 = Dropout(rate=0.2)(x1)\n",
        "x1 = Dense(64, activation='relu')(x1)\n",
        "output1 = x1\n",
        "modeldrug = Model(input1, output1)\n",
        "modeldrug.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Define the layers for the second branch of the network\n",
        "x2 = Dense(512, activation='relu')(input2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(256, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(128, activation='relu')(x2)\n",
        "x2 = Dropout(rate=0.2)(x2)\n",
        "x2 = Dense(64, activation='relu')(x2)\n",
        "output2 = x2\n",
        "modeldisease = Model(input2, output2)\n",
        "modeldisease.compile(loss='kl-divergence', optimizer=SGD(learning_rate=0.0005), metrics=['accuracy'])\n",
        "# Subtract the embeddings from the two branches\n",
        "diff = Subtract()([output1, output2])\n",
        "diff = Lambda(lambda d: abs(d))(diff)\n",
        "# diff = Dropout(0.2)(diff)\n",
        "# Concatenate the absolute difference with the embeddings\n",
        "# concat = concatenate([output1, output2])\n",
        "concat = Dense(32, activation= 'relu')(diff)\n",
        "# concat = Dropout(rate=0.2)(concat)\n",
        "\n",
        "\n",
        "# Define the output layer\n",
        "output = Dense(1, activation='sigmoid')(concat)\n",
        "\n",
        "# Define the model\n",
        "simmodel = Model(inputs=[input1, input2], outputs=output)\n",
        "\n",
        "# Define the optimizer and compile the model\n",
        "optimizer = Adam(learning_rate=0.00005)\n",
        "simmodel.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "\n",
        "# # Define the class weights\n",
        "class_weights = {0: 1, 1: 1966/748 } # inverse frequency weighting\n",
        "\n",
        "\n",
        "# Train the model on your data with the defined callbacks\n",
        "\n",
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "\n",
        "x_train, x_val, y_train, y_val = train_test_split(X, y)\n",
        "\n",
        "l = np.array(x_train[:, :1024])\n",
        "r = np.array(x_train[:, 1024:])\n",
        "\n",
        "\n",
        "l_val = np.array(x_val[:, :1024])\n",
        "r_val = np.array(x_val[:, 1024:])\n",
        "\n",
        "history = simmodel.fit([l, r], y_train, batch_size=32, epochs=100,validation_data=([l_val, r_val], y_val), callbacks=[early_stopping, reduce_lr, model_checkpoint])\n",
        "\n",
        "# history = simmodel.fit([l, r], y_train, batch_size= 32, epochs= 300, class_weight=class_weights, validation_data=([l_val, r_val],y_val)\n",
        "                  # ,callbacks=[callback]\n",
        "                  # )\n",
        "model_history(history)\n",
        "\n",
        "\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "EmU8BBJAx_v9"
      },
      "source": [
        "Now, we calculate the association score between our disease and all drugs to find the first 10 reltaed drugs to them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8pfy6_kx_v9",
        "outputId": "aeb6e2bb-d9de-476a-b3f3-6f73c90d2e3e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "13/13 [==============================] - 0s 3ms/step\n"
          ]
        }
      ],
      "source": [
        "covid_disease=['C0206750']\n",
        "xcovid=[]\n",
        "SCOR_PD=pd.DataFrame(columns=[\"Dis_id\", \"Drug_id\", \"Score\"]) # To save association score\n",
        "for dis in covid_disease:\n",
        "  for drug in my_drug_list:\n",
        "    xcovid.append(np.concatenate((model1.wv[(drug).lower()], model1.wv[(dis).lower()]), axis=None))\n",
        "\n",
        "xcovid= np.array(xcovid)\n",
        "lcovid = np.array(xcovid[:, :1024])\n",
        "rcovid = np.array(xcovid[:, 1024:])\n",
        "i=0\n",
        "s = simmodel.predict([lcovid, rcovid])\n",
        "for dis in covid_disease:\n",
        "  for drug in my_drug_list:\n",
        "    df2 = {\"Dis_id\":dis, \"Drug_id\":drug, \"Score\":s[i]}\n",
        "    i+=1\n",
        "    SCOR_PD = SCOR_PD.append(df2, ignore_index = True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "LscCtcoyyxtl",
        "outputId": "bb9539b6-23bb-4aed-ced8-dccab852d726"
      },
      "outputs": [
        {
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              "      <th>Dis_id</th>\n",
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              "      <td>[0.43762496]</td>\n",
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              "      <th>366</th>\n",
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              "      <td>[0.43102533]</td>\n",
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              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>C0206750</td>\n",
              "      <td>DB00413</td>\n",
              "      <td>[0.43032923]</td>\n",
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            "text/plain": [
              "       Dis_id  Drug_id         Score\n",
              "97   C0206750  DB00240   [0.9820702]\n",
              "0    C0206750  DB01047   [0.9820264]\n",
              "116  C0206750  DB00838  [0.98114395]\n",
              "7    C0206750  DB02703  [0.98110193]\n",
              "271  C0206750  DB01130  [0.98105294]\n",
              "..        ...      ...           ...\n",
              "298  C0206750  DB01175   [0.4518802]\n",
              "226  C0206750  DB00268   [0.4477359]\n",
              "19   C0206750  DB00215  [0.43762496]\n",
              "366  C0206750  DB00230  [0.43102533]\n",
              "60   C0206750  DB00413  [0.43032923]\n",
              "\n",
              "[410 rows x 3 columns]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "SCOR_PD = SCOR_PD.sort_values(by='Score', ascending=False)\n",
        "SCOR_PD"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
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        },
        "id": "lUc7raFCzEhw",
        "outputId": "35907667-f1c0-4d6f-fb2e-78b1590ea513"
      },
      "outputs": [
        {
          "data": {
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              "  <tbody>\n",
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              "      <th>151</th>\n",
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              "      <th>82</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "       Dis_id  Drug_id         Score\n",
              "151  C0206750  DB01234  [0.97374773]\n",
              "406  C0206750  DB00959   [0.9699562]\n",
              "82   C0206750  DB01394  [0.96028805]\n",
              "205  C0206750  DB00608    [0.955688]\n",
              "177  C0206750  DB00207    [0.914545]\n",
              "287  C0206750  DB08868  [0.90901476]\n",
              "237  C0206750  DB01050  [0.86170983]"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = SCOR_PD.loc[SCOR_PD[\"Drug_id\"].isin(my_covid_drug)]\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "fA6H2QuM3j6w",
        "outputId": "6b011c06-5831-430b-da29-73ecc663bbba"
      },
      "outputs": [
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            "text/plain": [
              "       Dis_id  Drug_id         Score\n",
              "336  C0206750  DB06203   [0.9642851]\n",
              "242  C0206750  DB04876  [0.96110183]\n",
              "148  C0206750  DB06335  [0.95970964]\n",
              "232  C0206750  DB08882   [0.9376126]\n",
              "15   C0206750  DB01261    [0.876006]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df2 = SCOR_PD.loc[SCOR_PD[\"Drug_id\"].isin(gliptins)]\n",
        "df2"
      ]
    }
  ],
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